HSE
Health & Safety
Executive
Cognitive Factors’ Influence on the Expression
and Reporting of Work-Related Stress
Prepared by Loughborough University, the University
of Nottingham and the Institute for Employment
Studies for the Health and Safety Executive 2003
RESEARCH REPORT 170
HSE
Health & Safety
Executive
Cognitive Factors’ Influence on the Expression
and Reporting of Work-Related Stress
Kevin Daniels BSc (Hons) PhD CPsychol ILTM
Loughborough University
David Jones, BSc (Hons) MSc and
Eamonn Fergusson, BSc (Hons) PhD CPsychol AFBPsS FRSH
University of Nottingham
Sarah Perryman, BA (Hons) MSc MA and
Jo Rick, BA (Hons) PhD CPsychol AFBPS
Institute for Employment Studies
There are questions concerning the extent to which individual differences, that is variation in factors
such as personality and attitudes, are responsible for the incidence of stress-related illness and
reporting of stress-related problems through questionnaires and other monitoring procedures
In the first part of this research, we conducted a meta-analysis on longitudinal studies of ill-health, work
conditions and individual differences. Results indicate that both individual differences and work
conditions are associated with increases in ill-health. By concentrating on the best possible longitudinal
research, this meta-analysis provides some of the strongest evidence to date that both adverse work
conditions and individual differences can cause stress-related illness.
In the next part of the research, we examined existing representative data-bases of the United
Kingdom population. We found individual differences that influence stress-related illness to be different
between genders, age groups, socio-economic groups and occupational groups, indicating differences
in risk of experiencing and reporting stress-related illness amongst these groups.
In the last part of this review, we examined some of the ways in which individual differences might
influence the experience and reporting of stress-related illness. Cognitive factors involved in the
experience of unpleasant emotions might play a central role in both the development and reporting of
stress-related illness.
This report and the work it describes were funded by the Health and Safety Executive (HSE). Its
contents, including any opinions and/or conclusions expressed, are those of the authors alone and do
not necessarily reflect HSE policy.
HSE BOOKS
© Crown copyright 2004
First published 2004
ISBN 0 7176 2770 5
All rights reserved. No part of this publication may be
reproduced, stored in a retrieval system, or transmitted in
any form or by any means (electronic, mechanical,
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written permission of the copyright owner.
Applications for reproduction should be made in writing to: Licensing Division, Her Majesty's Stationery Office, St Clements House, 2-16 Colegate, Norwich NR3 1BQ or by e-mail to [email protected]
ii
Acknowledgements
First, we would like thank Dr Rob Briner, Prof Tom Cox CBE and Prof Philip Dewe – who
formed a panel of external advisors - for their advice and encouragement during the early
stages of this research. We are indebted to their knowledge of research and practice into
occupational stress.
We would also like to thank others that responded to requests for material and advice on the
specific questions of our review, namely: Prof Cary Cooper CBE, Dr Jan de Jonge, Prof
Stanislav Kasl, Prof Roy Payne, Dr Katherine Parkes, Prof Dr Wilmar Schaufeli, Prof Paul
Spector, Dr Jukka Vuori, Prof Peter Warr and Prof Jerry Wofford. Not only did these provide
material invaluable as background reading for this report, but also they responded with
enthusiasm for this undertaking.
Finally, from the HSE, we would like to thank our project manager Prof Colin Mackay, Dr
Simon Clarke and Steve Lee for their support and help with this research.
The work reported here was undertaken while Kevin Daniels was with the University of
Nottingham.
iii
iv
Contents
1
Executive Summary
1
2
Introduction
3
2.1. Aims of the project
4
2.2. Limitations of considering only hazards as an
explanation for occupational ill-health
6
2.3. Cognitive factors
7
2.3.1. What cognitive factors influence
2.3.1.1.
Expert panel
2.4. Individual differences
9
2.4.1. What is an individual difference? Traits,
attitudes and states 10 2.4.2. How individual differences are linked to stress
12 2.4.3. One major orientation to stress
13 14 2.5.1. Psychosocial hazards
14 2.5.2. Health and related variables
16 2.6. Structure of report
17 Quantitative review: meta-analysis
18
3.1. Searching the literature.
21 3.1.1. Inclusion criteria
24 3.1.1.1.
10 2.5. Psychosocial hazards and health
3
9
The search terms
3.1.2. The scale of the literature and sifting the
literature 3.2. Meta-analysis
25 26 30 3.2.1. A brief introduction to meta-analysis and
correlation coefficients
30 3.2.2. Coding the papers
36 3.2.3. Findings
39 3.2.3.1.
Individual differences and ill-health
40 3.2.3.2.
Hazards and ill-health
42 v
4
3.2.3.3.
Stability in health measures
44
3.2.3.4.
Partial correlations 45 3.3. Summary and implications of the meta-analysis
46 Secondary analysis of existing data-bases
51 4.1. Data-bases 51 4.1.1. Health Survey for England 2000 (HSfE)
51 4.1.2. Health and Lifestyle Survey 1991/2 (HALS2)
52 4.1.3. Procedure 52 4.2. Analysis
5
54 4.2.1. Analyses for HSfE2000 54 4.2.2. Analyses for HALS2 58 4.3. Summary and conclusions 64 4.3.1. Conclusion 66 Discussion
67
5.1. Paths from individual differences to occupational illhealth
68
5.1.1. Affective Events Theory
69 5.1.2. Lazarus, appraisal and coping
70 5.1.3. Action-control theory and goals 71 5.1.4. CAPHEM model
74 5.2. Individual differences & self-report measures:
correlation inflation 79 5.2.1. Occasion factors 79 5.2.2. How mood and disposition interact to
determine how we notice psychosocial hazards 80 5.3. Change & stability in psychological distress 80 5.3.1. Change & stability explained in the CAPHEM model
81 5.3.2. Small effects sizes and large changes: vicious
and virtuous circles 82 5.4. Individual differences and the experience of physical
symptoms
83 5.4.1. The role of negative orientation in the vi
83 perception of symptoms
5.4.2. The role of negative orientation in the
presentation and diagnosis of symptoms 84 5.4.3. Negative orientation and health – is there a
really a link?
86 5.5. Individual differences and socio-cultural factors
6
Conclusion
87 90
6.1. Implications for monitoring
91 6.1.1. Are measures too insensitive?
92 6.1.2. How to assess negative orientation
93 6.2. Implications for practice
93 6.2.1. Cognitive and perceptual factors and the
individual
93 6.2.2. Cognitive and perceptual factors and society
95 6.3. Implications for research
96 6.3.1. Need for detailed, time intensive studies
96 6.3.2. Researching cognitive factors in the workplace
97 6.4. Concluding remarks
99 References
100
Appendices:
107 A) Form used to sort studies
107 B) Bibliography for CAPHEM model
114 vii
viii
1. Executive Summary
There is debate over the extent to which psychosocial
hazards really influence occupational ill-health, or
whether findings from research really reflect individual
differences, related to factors such as personality and
attitudes. That is, the debate centres on whether work
conditions, individual differences, or a combination of
both influence stress-related illness. This debate has
many implications: I) for our understanding of workrelated stress; II) for the extent to which changes in
organisational practice can reasonably be expected to
reduce stress-related illnesses; and III) for the means
of assessing stress-related illness to gauge the
success or otherwise of interventions.
In this research, we seek to examine some aspects of
this debate and outline some of the implications for
policy and organisational practice.
In the first part of this research, we conducted a metaanalysis on longitudinal studies of ill-health, work
conditions and individual differences. Results indicate
that:
· In general, both individual differences and adverse
work conditions are associated with increases in illhealth.
· Adverse work conditions are associated more
closely with subsequent increases in psychological
symptoms than subsequent increases in physical
symptoms or changes in health behaviours;
· Individual differences are associated with
subsequent increases in the experience of
psychological symptoms and perception of physical
symptoms;
· There is a good deal of stability in psychological
health.
By concentrating on the best possible longitudinal
research, this meta-analysis provides the some of the
strongest evidence to date that both adverse work
conditions and individual differences can cause stressrelated illness.
In the next part of the research, we examined existing
representative data-bases of the United Kingdom
population.
We found individual differences that
influence stress-related illness to be different between
genders, age groups, socio-economic groups and
occupational groups, indicating differences in risk of
1
experiencing and reporting
amongst these groups.
stress-related
illness
In the last part of this review, we examined some of the
ways in which individual differences might influence the
experience and reporting of stress-related illness.
Taking into account the results of the meta-analysis, we
conclude that that cognitive factors involved in the
experience of unpleasant emotions might play a central
role in both the development and reporting of stressrelated illness.
A number of implications arise from the findings of this
report:
· Monitoring systems assessing psychological health
are more likely to be more sensitive to changes in
the work environment than those assessing
physical health. However, some monitoring systems
might overestimate the extent of stress-related
illnesses attributable to work, especially in some
demographic groups in the population.
· A greater emphasis needs to be placed on how
individuals interpret the work environment in
practice, for example in conducting psychosocial
risk assessments, focusing interventions on how
individuals pursue and attain goals, and in
communication about the risks from psychosocial
hazards.
· Research should place greater emphasis on the
cognitive processes underpinning interpretation of
the work environment and physician diagnosis of
stress-related illnesses.
2
2. Introduction
This research was undertaken on behalf of the HSE to
provide a review of scientific evidence in relation to the
cognitive processes – that is perceptual, mental and
related psychological processes - involved in the
expression and reporting of stress-related symptoms and
ill-health.
In broad terms, we took this to mean identifying the
cognitive processes not only involved in the stress­
response, but also identifying the cognitive processes
that influence the expression and reporting of stress­
related psychological and physical symptoms. Indeed,
it is possible to differentiate between the ‘experience’,
‘expression’ and ‘reporting’ of work-related stress and
stress-related symptoms and illness. Therefore, in the
context of this report:
‘experience’ refers to the cognitive experience of
symptoms;
‘expression’ refers to overt displays of those symptoms,
particularly to medical practitioners;
‘reporting’ refers to completion of self-report
assessments in epidemiological surveys, evaluations of
workplace interventions etc.
These descriptions illustrate that there are important
distinctions to be made to fulfil the objectives of the
research. For example, factors – like a tendency to
worry – might lead some people to amplify their
experience of symptoms, which might then lead to over­
reporting of stress-related symptoms in epidemiological
surveys – thus giving a false picture of the size of any
stress-related problem and, what, if anything needs to be
done. Conversely, factors that might mask expression
of stress-related symptoms, such an optimism, may lead
to physicians to attribute symptoms to a source other
than work-related stress. This then might lead to under­
reporting of stress-related illnesses in the population as
assessed by surveillance schemes that rely on reports
from physicians – again giving a false picture.
Therefore, it is vital that the research distinguishes
between the cognitive attributes related to different
aspects of the experience, reporting and expression of
symptoms.
3
In the rest of this chapter, we outline the aims of this
research; define and describe cognitive processes in
greater detail; explain why studies of individual
differences help us identify the influence of cognitive
processes; and explain our approach to the rest of the
research.
2.1.
Aims of the project
HSE set four objectives for this research.
1) Identify the principle factors that modify the
reporting of the response to stress.
We took this to mean that the research needed to make
explicit the following:
a) how the expression of work-related health outcomes
is influenced by a range of psychological attributes;
b) how these psychological attributes influence various
cognitive processes;
c) how these psychological attributes may influence
monitoring of stress-related illnesses in the population
and in relation to interventions.
Point a) is considered in the next chapter, that describes
a meta-analysis of longitudinal studies. Points b) and c)
are discussed in more detail in the fourth and fifth
chapters. We also undertook to examine how these
psychological attributes vary in the UK population (e.g.
examine whether differences exist in different
occupational groups). We did this by analysing existing
data-bases representative of the UK population. In
doing so, we were able to establish whether cognitive
factors might accentuate or attenuate the experience and
reporting of stress-related symptoms in different
demographic groups.
2) Assess the impact of each of the psychological
variables on the reporting of stress responses.
We took this to mean that the report should make clear
statements about the causal processes by which
principal psychological variables, for example low self­
esteem, influence expression and reporting. This
emphasis on causality meant that we made very strict
choices about the studies that we included in the meta­
analysis. We also took this to mean that we needed to
establish numerical estimates of the strength of the
4
relationships between psychological attributes and the
expression and reporting of stress-related symptoms and
illness. By establishing such estimates – known as
effect sizes - it will be possible to obtain better
estimates of the influence of changes in work
environments on stress-related symptoms and illness.
Because the technique of meta-analysis provides a
means of establishing such effect sizes, then we chose
to give greatest weight in our research to meta-analysis
of existing, rigorous evidence.
3) Describe how cognitive factors are implicated in the
generation of the stress process.
We took this to mean that the research should attempt to
explain why relationships might exist between
psychological attributes and various elements of the
experience, reporting and expression of stress-related
symptoms, as well as establishing numerical estimates
of those relationships. Therefore, the penultimate
chapter of this report, informed by the results of the
meta-analysis, reviews some relevant theoretical
explanations of cognitive factors in the occupational
stress process.
4) Give some indication of the temporal stability of the
factors identified would also be useful.
We took this to mean three things:
a) whether each of the attributes that we identify are
subject to change;
b) whether those attributes subject to change are
subject to longer term and relatively permanent change,
and/or whether they are subject to short-term
fluctuations around a relatively stable average level for
each person;
c) the processes by which more enduring changes may
come about.
Points a) and b) are investigated in this report, by
drawing on the results of our own meta-analysis and the
results of other research. Point c) is considered in
chapter 5, where we discuss the processes underlying
change and stability in the work-related stress process
and, also, the role of wider socio-cultural variables in
influencing the cognitive processes underlying work­
related stress.
5
In addition to these specific objectives, the HSE also
requested that we include description of any
uncertainties in the evidence base and draw attention to
areas where the evidence is strong or weak. In this
respect, in the last chapter, we provide an indication of
how research in this area might usefully proceed.
2.2.
Limitations of considering only hazards as an explanation for
occupational ill-health
Researchers have identified many work conditions related
to health at work (Warr, 1999), yet variation in work
conditions typically accounts for only small proportion of
variation between people in levels of well-being (Rick,
Thomson, Briner, O'Regan and Daniels, 2002). One
reason for this is because individuals exhibit different
reactions to seemingly similar environments (Campbell,
Chew and Scratchley, 1991)
Therefore, the experience of stress-related symptoms is
not always an inevitable consequence of exposure to
adverse work conditions in the workplace.1 Neither
does it follow that the expression of those symptoms or
reporting of those symptoms follow from the experience
of those symptoms, as it is also possible that there exists
variation between individuals in these relationships.
For example, some people might suppress how they feel
when interacting with others, and others might more
easily remember episodes of poor well-being or illness
when reporting on absence from work over the previous
year in surveys.
The issue, then, becomes what additional factors do we
need to take into account to explain individual variation
in these relationships. Our view is that cognitive factors
– which we describe in detail in the next section – are
the best candidates for explaining individual variation.
We base this assertion on four main arguments.
a) Stress and related concepts such as emotions are
fundamentally experiential phenomena (Lazarus,
1999) and any explanation of stress-related
phenomena needs to take into account the mental or
cognitive processes that shape these phenomena;
1
There exists variation in the terminology used to describe adverse work conditions
in relation to occupational stress. Some authors prefer the term ‘stressors’ and
others ‘psychosocial hazards’. HSE itself uses the term ‘psychosocial hazards’.
6
b) Lazarus’ appraisal theory is one of the most
influential accounts of stress and emotions (Cooper,
Dewe and O’Driscoll, 2001). This theory is
fundamentally cognitive, and many other
contemporary theories of stress and emotions build
explicitly on Lazarus’ approach (see e.g. Power and
Dalgleish, 1997).
c) Within the science of psychology as a whole,
cognitive approaches have been dominant for a
number of decades. This is an impressive feat for a
young science whose institutions were established
within the past 150 years. The dominance of
cognitive approaches reflects their success in
explaining aspects of many areas of psychological
experience and informing much applied
psychological practice;
d) There is a developing body of theory and evidence
that indicates key aspects of the work-related stress
process can be explained and new interventions
formulated by drawing upon experimentally verified
principles of human mental activity (Daniels, 2001;
Daniels, Harris and Briner, 2002a; 2002b; 2002c; in
press).
2.3.
Cognitive factors
Cognition is a term that, broadly, refers to the mental
processes of perception, attention, memory, learning,
problem solving and decision making (Eysenck and
Keane, 1990). In cognitive approaches to stress and
emotions (Daniels et al, 2002b, in press; Power and
Dalgleish, 1997), cognitive or mental models play a key
role. To the extent that different people have different
views on the world, and therefore different mental
models, how mental models influence stress, emotions
and health is important for understanding individual
variation in response to psychosocial hazards at work.
We describe and define mental models next.
At any given time, we can only pay attention to a
limited amount of information. For example, when
taking a decision at work, we are constrained in being
able to attend only to some, not all, relevant
information.
Moreover, in work situations, we often have to make
sense of ambiguous, conflicting and complex
information. To do this, we rely on experience and
learning to help us select the most relevant information
7
to attend to and to interpret this information. Therefore,
we not only use information from the environment, but
also our memory when taking decisions.
To use our memory, we derive our own ‘lay’ theories
of how the world works. These represent our
knowledge, which is accumulated through learning and
experience. These theories are simplified, generalised
and abstract representations and go under many names 'mental models' being one (after Johnson-Laird, 1989).
People develop their ‘mental models’ through a set of
organising principles that group associated concepts
together: Mental models can contain knowledge that
provides categories or labels (e.g. ‘Unpaid overtime is
an unreasonable imposition’), or about processes (e.g.
‘Talking to Susan usually makes me feel better’). When
we are confronted with a situation, we simply recall the
mental models that seem most appropriate to that
situation at that time. Once recalled, the mental model
then helps to identify which information is most
important for the task at hand, how to interpret that
information and so, eventually influencing our
behaviour.
For instance, mental models have been shown to
influence subsequent predictions about future events,
decision making and communication (Daniels and
Henry, 1998). Crucially for stress-related phenomena,
mental models are also thought to influence our
emotions by influencing how we interpret or appraise
work conditions and influencing how we cope with
aversive conditions and events (see e.g. Power and
Dalgleish, 1997).
A mental model does not have to be in the conscious
mind to influence subsequent cognition and behaviour.
In some instances, recall may be entirely unconscious
and we apply a mental model to a situation without
being aware of it (Dutton, 1993).
Further, people synthesise mental models, rather than
simply recalling them from memory – they recall
several generalised and abstracted mental models and
combine them in new ways to be relevant to the given
context (Barsalou, 1982).
For example, in responding to heavy work demands, we
may recall knowledge about how work demands
influence what we want to achieve at work, synthesising
this with knowledge about how work demands
8
influence our feelings and how we have coped with
heavy work demands in the past. This helps us form a
mental model of the likely consequences of heavy work
demands and what forms of coping might be
appropriate (cf. Daniels et al, in press).
2.3.1. What cognitive factors influence
As noted, it is possible to differentiate between, for
example, the experience, expression and reporting of
work-related stress and stress-related symptoms. It is
also possible to differentiate, for example, between
processes that act independently of psychosocial
hazards and those that mediate or moderate the
influence of psychosocial hazards (e.g. Moyle, 1995).2
After examining major organising frameworks in the
literature (e.g. Schuler, 1985) it is also possible to
derive a set of relationships with a range of variables
related to health that are thought to be conjointly
influenced by cognitive processes and psychosocial
hazards.
2.3.1.1. Expert panel
At an early stage in this project, we assembled this
research team with a panel of experts to discuss possible
relationships.3 Taking into account direct influences,
moderated and mediated relationships, and being as
inclusive as possible, it was felt that cognitive factors
related to work-related stress could influence:
i)
Acute psychological states, such as mood;
2
In statistical terms, a mediated relationship is one where a change in one variable
causes a change in another variable, which subsequently causes a change in a third
variable. For example, an increase in work demands might lead to feelings of being
overwhelmed and without control at work, which in turn leads to depression. A
moderated relationship is one where the relationship between two variables is
dependent upon another variable. For example, the relationship between discretion
over how to complete work tasks and job satisfaction might be dependent upon
self-esteem. Those people with low self-esteem might have less confidence in
their own ability to make decisions about their job and thus will be more satisfied
with work than those with high self-esteem when any discretion over how work
should be performed rests with their supervisor.
3
The expert panel consisted of this report's authors, excepting Sarah Perryman and
our expert advisors, namely Dr Rob Briner (University of London), Dr Simon
Clarke (HSE), Prof Tom Cox CBE (University of Nottingham), Prof Philip Dewe
(University of London), and Prof Colin MacKay (HSE).
9
2.4.
ii) Chronic psychological states, such as
depression;
iii)
Acute physical symptoms such as headaches;
iv) Chronic physical conditions such as
hypertension;
v) The reporting of psychological and physical
states, so that self-reports of psychological and
physical symptoms amplify or attenuate
measurement of these symptoms by self-report;
vi) Health-related behaviours, such as smoking,
drinking and sickness absence from work;
vii) Physicians’ diagnoses of symptoms as more or
less stress-related.
Individual differences
Since individual differences in responses to
psychosocial hazards at work gave rise to this project, it
is hardly surprising that there is both a great deal of
research on individual differences in the stress process
(e.g. Payne and Cooper, 1991) and that individual
differences can inform us of the nature of the cognitive
processes related to work-related stress.
2.4.1. What is an individual difference? Traits,
attitudes and states
An individual difference, in the context of this research,
was defined simply as psychological variables upon
which individuals may differ. Of course, we limited our
consideration of individual differences to those thought
to have an effect on work stress, health or related
behaviour.
It is possible to differentiate three major classes of
variables that reflect individual differences. These are:
a) Traits, such as the Big 5 personality factors of
neuroticism, extraversion, openness to experience,
agreeableness and conscientiousness (e.g. McCrae and
Costa, 1997). These are thought to be relatively
impervious to change after the onset of early adulthood.
They are considered to have a major genetic
component;
10
b) Attitudes, beliefs and related concepts, such as locus
of control, Type A Behaviour Pattern, self-esteem and
job satisfaction (see e.g. Payne and Cooper, 1991 for
summaries of such variables in relation to work-related
stress). These variables are characterised as being more
fluid than traits, and, therefore, more capable of
changing after early adult-hood – even if they do
contain a moderate to large trait component (e.g.
Dormann and Zapf, 2001).
c) State variables, such as mood and emotions.4 These
variables might be influenced by traits and attitudes, but
are very fluid and can change very rapidly (Parkinson,
Briner, Reynolds and Totterdell, 1995).
The importance of making this differentiation with
respect to individual differences is that the more trait
and attitudinal individual differences can be considered
indices of stable cognitive ‘structures’. These provide a
backdrop to the cognitive processes underlying the
experience of stress, whilst state individual differences
can be considered closer to the cognitive processes as
these processes unfold (cf. Lazarus, 1999).
It is obvious from our classification that some attitudes
and states (e.g. job satisfaction, anger) are often studied
as outcomes of the stress process rather than causes of
stress. However, many theoretical models of stress
indicate that many supposed outcomes can influence
some causes (Edwards, 1992) and evidence indicates
such outcomes influence other attitudes, the
interpretation of the stressfulness of psychosocial
hazards and, possibly, even the occurrence of some
psychosocial hazards (Daniels and Guppy, 1997).
Demographic variables represent one form of individual
difference, although we did not examine demographic
variables in the review for two reasons:
a) Demographics are, at best, distal indicators of
individual differences in cognitive and perceptual
processes – and even then, they are more likely to
represent socio-cultural factors (Lazarus, 1999), which
are beyond the scope of this research. Nevertheless, the
secondary analyses described later in this report give
4
In the psychological sciences, the term 'emotion' refers to feelings towards an
event, object or person, whilst the term 'mood' refers to feelings that cannot
necessarily be linked to a specific event, object or person. The term 'affect' is a
more general term that subsumes both 'mood' and 'emotion' (see Parkinson, 1995).
11
some indication of the extent of variation in the
individual differences studied here amongst
demographic groups in the UK population.
b) The scale of the relevant literature. Literature
searches for relevant articles with demographic
variables identified over 3373 articles published in the
past five years alone. Within the time limits set for this
research, it was not then possible to incorporate
demographic factors into the meta-analysis.
2.4.2. How individual differences are linked to
stress
The cognitive processes linking individual differences
to stress are based on mental models. For traits and
attitudes, certain psychological characteristics, such as
neuroticism, a tendency to worry and low self-esteem,
are associated with a larger number of mental models
about the negative consequences of work environments.
These mental models are also more elaborate (Wofford
and Daly, 1997).
People with negative psychological traits and attitudes
are more likely to recall negative information about
work environments – meaning they are more likely to
notice negative aspects of the work environment and
more likely to engage in further deliberation and
processing of that information in a way that reflects an
elaborate negative interpretation.
Consequently, it could be expected that people with
certain psychological characteristics could feel worse
about work, may react more strongly to psychosocial
hazards – psychologically and physiologically - and
might amplify their experience of physical symptoms.
Current mood or emotion is thought to interact with
these psychological characteristics (Williams, Watts,
MacCleod & Mathews, 1997), so that negative moods
or emotional states cue the influence of mental models
that reflect negative interpretations of the environment.
Put another way, people in a negative mood or
emotional state are more likely to recall negative
information or notice negative aspects of the
environment. Again, then, it could be expected that
people with in a negative mood or emotional state might
react more strongly to psychosocial hazards and amplify
their experience of symptoms.
12
2.4.3. One major orientation to stress
To impose order on a vast literature, we decided to
address individual differences on a broad continuum of
positive to negative orientation, differentiating traits,
attitudes and states within this broader dimension.
The reasons for doing this are threefold.
First, there is evidence that different parts of the brain
are biased toward processing negative or positive
emotional information (Watson, Wiese, Vaidya, and
Tellegen, 1999).
Second, whilst work-related emotional or affective
well-being has several distinct aspects (e.g. anxiety,
depression), the major differentiation between aspects
of affective well-being at work is their pleasantness or
unpleasantness (Daniels 2000; Warr, 1990).
Third, a number of studies indicate that a constellation
of attitudes and traits co-occur that indicate proneness to
stress, negative emotional states and disease (Friedman
and Booth-Kewley, 1987; Wofford and Daly, 1997).
As a result, we are able to classify the individual
differences examined in this report according to their
temporal stability (trait, attitude, state) and whether they
represent a more negative or positive orientation toward
the work or general social environment. Table 2.1.
illustrates this framework.
Table 2.1. Types of individual difference in relation to occupational stress
Trait
Attudinal
State
+ve orientation
e.g. agreeableness
e.g. high selfesteem
e.g. enthusiasm
-ve orientation
e.g. neuroticism
e.g. Type A
(coronary prone)
Behaviour Pattern
e.g. anger
NB. +ve to –ve orientation reflects a continuum. For example, high scores on a measure of neuroticism
represent a negative orientation, low scores a positive orientation and scores in the mid-range represent
an orientation between the two extremes.
13
2.5.
Psychosocial hazards and health
Since the purpose of this report is to examine individual
differences with respect with work-related stress and
stress-related symptoms, it is necessary to give some
consideration to characteristics of the work environment
that can cause stress – so-called stressors or
psychosocial hazards, and the nature of our outcome
measures – namely health and related variables.
2.5.1. Psychosocial hazards
The HSE have commissioned research on psychosocial
hazards to help them define minimum management
standards in nine main areas (see e.g. Rick et al, 2002).
These areas correspond to some of the major categories
of psychologically aversive work conditions thought to
cause ill-health. They have been operationalised by the
research team conducting this research as:
1) Work demands - including:
1a) workload - quantity, pacing, time pressure,
emotional content of work;
1b) work scheduling – total hours, breaks, travel
time, shift work;
1c) work organisation and job design – task design,
team working;
1d) physical environment – real and perceived
danger, noise, toxins;
2) Lack of worker control or autonomy – including:
2a) Lack of skill discretion – variety, opportunity
for skill use;
2b) Lack of decision authority – control and
autonomy;
2c) Lack of participation in decision making.
3) Lack of support and interpersonal relationships in the
working environment – including:
3a) Lack of proactive support – practical &
emotional support, support from work and outside
of work, feedback;
14
3b) Lack of reactive support - practical & emotional
support, support from work and outside of work,
feedback;
3c) Lack of team/interpersonal conflict, bullying
and harassment;
We expected that most of the studies we reviewed
would include psychosocial hazards that could be
classified according to these three major categories:
work demands; lack of work control and lack of work
support. However, we also anticipated some studies
would include measures of other stressful work
conditions – such as job insecurity.
One issue in the literature concerns whether
psychosocial hazards are assessed objectively or
assessed by self-reports. One line of reasoning is that
self-reports are more likely to biased by individual
cognitive factors, and so do not give an accurate picture
of the influence of the work environment on health.
Another line of reasoning asserts that the person best
able to give an accurate assessment of a person’s work
environment is that person him or herself, and so self­
reports might give a more accurate picture than other
methods. A related concern is that psychosocial
hazards such as control and support do not exist in any
real, material sense, and therefore can only be assessed
by subjective methods.
In the meta-analysis, then, we differentiated between
studies that used subjective self-report methods and
studies that used other, more objective methods to
assess psychosocial hazards. The more objective
methods could include the following:
a) aggregate self-reports across all workers for a given
job;
b) managerial reports;
c) observations by members of the research team;
d) inferred job characteristics from analysis of job titles
or job descriptions;
e) inferred job characteristics from organisational
change interventions (e.g. introduction of semi­
autonomous work teams);
f) some combination of the above.
15
2.5.2. Health and related variables
To recap, in the context of this research, we take:
‘experience’ to refer to the cognitive experience of
symptoms;
‘expression’ to refer to overt displays of those
symptoms, particularly to medical practitioners;
‘reporting’ to refer to completion of self-report
assessments in epidemiological surveys, evaluations of
workplace interventions etc.
It is then necessary to include in our review indicators
of each of these manifestations of stress-related
symptoms. The approaches taken to measurement can
include self-reports, physician reports or more
‘objective’ measures taken by the research team,
including physiological indices and assessment by
independent medical practitioners. We might expect
that:
a) Self-reports tap both ‘experience’ and ‘reporting’ of
symptoms;
b) Physician reports tap ‘experience’, ‘reporting’ and
‘expression’;
c) Objective measures or independent diagnosis by a
research team tap ‘experience’.
In this respect, by comparing across studies that use
different methods, it might be possible, to some extent,
to disentangle the influence of experience of stress and
related symptoms from the expression and reporting of
stress and related symptoms.
Given that stress-related ill-health might encompass
physical and psychological ill-health, then we must also
consider studies that assess physical health,
psychological health or both. It is also possible to
distinguish between acute and chronic illness.
Finally, we have also included health-related behaviours
for consideration (such as visits to the physician, self­
reports of medication use etc). This might give us
further insight into the reporting of stress-related
symptoms.
Table 2.2 shows the classification of health measures
used as dependent variables in this review.
16
Table 2.2. Typology of health indices.
Measurement Self-reports
Health state
Acute psychological
Acute physical
Chronic psychological
Chronic physical
e.g. Emotions
e.g. Acute anxiety
disorder
e.g. Non-specific
symptoms such as
headaches
e.g. Psychiatric
screening surveys
such as the Beck
depression
inventory
e.g. Self-reports of
angina
e.g. Influenza
Health behaviours
2.6.
Physician-reports
e.g. Self-reported
drinking
e.g. Diagnosed
depression
e.g. Hypertension
e.g. Visits to
physician
Objective
measures/research
team diagnosis
e.g. Diagnosis by
research team
psychiatrist(s)
e.g. Swabs taken
for presence of flu
bugs
e.g. Diagnosis by
research team
psychiatrist(s)
e.g. Hypertension
measured by
physiological
indices
e.g. Objective
measure of alcohol
consumed
Structure of report
The rest of the report consists of four chapters:
Chapter three describes the major part of the project: a
meta-analysis to identify the role of individual
differences in the work-related stress process.
Chapter four is concerned with identification of
variation in individual differences that influence stress
in the UK population.
Chapter five is concerned with explaining the findings
of the third and fourth chapters with reference to theory
and other evidence.
Chapter six draws conclusions from this research with
respect to implications for monitoring and measurement
of work-related stress and associated symptoms; policy
and practice; and research.
17
3. Quantitative review: meta-analysis
Meta-analysis is a statistical technique for aggregating
information across several studies to provide better
estimates of relationships between two or more
variables. In this way, meta-analysis is able to
capitalise on larger sample sizes. It is also able to
compensate for some methodological weaknesses of
studies directly. As such, unlike more traditional
reviews, meta-analysis allows researchers to provide
quantitative estimates of relationships.
The purpose of this chapter is to outline the
methodology used to identify studies to include in the
meta-analysis and to convey the findings.
To recap, to identify cognitive and perceptual processes
that influence the experience, expression and reporting
of stress-related symptoms, it is necessary, first, to
identify individual differences that provide variation in
those cognitive and perceptual processes. By
identifying such individual differences, it is possible to
infer the differences between individuals in the
cognitive processes that underpin the experience,
expression and reporting of stress-related symptoms.
Theoretical knowledge can be used to help these
inferences.
Consequently, we have focused our review of the
literature around six main questions5:
RQ1) To what extent do individual differences have an
influence on subsequent health-related variables in
working samples?
RQ2) Which of trait, attitudinal or state individual
differences has the strongest influence on health-related
variables in working samples?
RQ3) How do individual differences influence the
reporting, experience and expression of health?
5
We were also interested in determining whether individual differences moderated
the relationships between psychosocial hazards and health-related outcomes.
However, only two studies that met our inclusion criteria have been published, and
both these studies reported information in such a way that we could not use them
for meta-analysis.
18
RQ4) To what extent do workplace psychosocial
hazards have an influence on subsequent health-related
variables?
RQ5) What are some of the influences on relationships
between health-related variables and psychosocial
hazards?
RQ6) How stable is psychological health?
We now explain how we addressed each research
question in more detail.
RQ1) To what extent do individual differences have an
influence on subsequent health-related variables in
working samples?
To answer this question, we aimed simply to assess the
strength of the statistical relationship between
individual differences and health-related variables in
working samples. We examined relations between
individual differences and subsequent health so that we
could be more confident that any relationship reflects
the extent to which individual differences cause health
(rather than changes in health changing individual
differences). Therefore, we examined only longitudinal
studies. We explain our reasoning in more detail in
section 3.1.1.
We also assessed the strength of the statistical
relationship above and beyond the influence of
psychosocial hazards on health as well as above beyond
the influence of initial health status, in order to rule out
some alternative explanations, such as that some
psychological characteristics (e.g. neuroticism) or poor
health make it more difficult for job applicants to be
selected for good jobs.
RQ2) Which of trait, attitudinal or state individual
differences has the strongest influence on health-related
variables in working samples?
To answer this question, we aimed simply to assess
whether the strength of the relationship between
individual differences and health variables is stronger or
weaker for traits, attitudes and state individual
differences.
19
RQ3) How do individual differences influence the
reporting, experience and expression of health?
To answer this question, we aimed to assess whether the
strength of the relationship between individual
differences and health varied according to kinds of
methods used to assess health and related variables. If,
for example, self-reports of physical health are more
strongly related to individual differences than physician
reports and research team diagnosis, then we can
conclude that cognitive factors influence the cognitive
experience or reporting of stress-related physical ill­
health, but not the expression of physical ill-health.
RQ4) To what extent do workplace psychosocial
hazards have an influence on subsequent health-related
variables?
Since we are interested in the role of individual
differences in the work context, it seems natural to
include this question. Again, since we are interested in
a causal relationship, we examined the influence of
psychosocial hazards on subsequent health. We also
assessed the extent of the relationship between
psychosocial hazards and health, above and beyond the
influence of individual differences and initial health
status. If a reliable statistical relationship remained
after taking into account individual differences and
initial health status, then we could be more confident
that psychosocial hazards do cause ill-health in some
way, rather than reflecting differences in how people
experience the work environment.
RQ5) What are some of the influences on relationships
between health-related variables and psychosocial
hazards?
To answer this question, we examined whether different
methods or measures of health and psychosocial
hazards influenced the strength of the relationship
between psychosocial hazards and health. If, for
example, there is a stronger relationship between
psychological health and psychosocial hazards than for
physical health, then we might conclude the influence
of the work environment on physical health is mediated
through psychological health.
RQ6) How stable is psychological health?
Assessing the stability of psychological health does give
some indication of the extent to which changes in the
20
work environment might lead to changes in
psychological health, at least over the shorter term.
Also, good psychological health is closely related to a
positive orientation, and poor psychological health to a
negative orientation – for example people nearing
clinical levels of poor psychological health are more
likely to rate their work demands as stressful (Daniels
and Guppy, 1997). Therefore, assessing the stability of
psychological health, as indexed by measures of job
satisfaction or anxiety for example, would help to
provide some assessment of the extent to which
influential individual differences that reflect a positive
to negative orientation are malleable. To do this, we
aimed to assess the strength of the relationship between
psychological health measured on two occasions.
In the rest of this chapter, we explain:
3.1.
i)
the procedures we used to search the literature
for relevant studies;
ii)
the procedures used in our meta-analysis;
iii)
the results of the meta-analysis.
Searching the literature.
To identify relevant journals, we surveyed the following
data-bases for the years 1996-2001 inclusive: Web of
Science, PsychInfo and MedLine. Additionally, we
conducted manual trawls for relevant articles in the
following journals, identified by consensus amongst the
research team, HSE advisors and members of our expert
panel as the top journals in this field. The journals
surveyed6 were:
Academy of Management Journal
Journal of Psychosomatic Research
Journal of Applied Psychology
Journal of Organizational Behavior
Journal of Occupational Health Psychology
Journal of Occupational and Organizational Psychology
6
Some journals were excluded from the manual review, although their quality was
felt to be high. They were excluded because their focus was too general and they
rarely carry material relevant to psychosocial hazards and occupational health.
The journals considered, but excluded, were Epidemiology, American Journal of
Epidemiology, International Journal of Epidemiology, American Journal of Public
Health, Journal of Personality and Social Psychology, Journal of Epidemiology
and Community Health, British Medical Journal.
21
Journal of Occupational and Environmental Medicine
Occupational and Environmental Medicine
Occupational Medicine
Psychological Medicine
Psychosomatic Medicine
Scandinavian Journal of Work, Environment and Health
Work & Stress
Articles of a theoretical nature, review articles or meta­
analyses that might inform the discussion of theoretical
paths linking individual differences to work-related
stress were also identified from this manual search. In
this respect, we extended our manual search from 1991­
2001 inclusive for such articles. Specifically to search
for theoretical and review articles, the following
journals were also examined manually for the years
1991-2001 inclusive:
Academy of Management Review
Psychological Bulletin
Psychological Review
We restricted our search to peer-reviewed, English
language journals only. We opted for English language
journals since a) none of the research team has the
requisite technical proficiency in other languages and b)
it is widely recognised that the majority of top journals
in the area are published in English, including some
continental European journals.
We restricted our search to peer-reviewed journals as
peer review provides an important quality control
process on the literature. By considering peer-reviewed
journals only, we can be confident of including only top
quality studies in the review.
The researchers were aware of a wide-spread suspicion
associated with publication bias in peer-reviewed
journals (towards positive results). However, given the
criticism of methodologies routinely employed in work­
related stress research (Kasl 1996; Zapf, Dormann and
Frese, 1996), we thought it unlikely that any publication
bias would make a significant impact on the rigour of
the review (see below). In addition, meta-analytic
procedures are capable of estimating the likely
influence of publication bias in a particular area (see
below).
Peer-reviewed studies were also chosen to help reduce
the significant number of papers. Very little is to be
gained by inclusion of non-peer-reviewed studies, since
22
there are enough peer-reviewed to provide stable
estimates of correlations – especially relative to the cost
that needs to be invested in finding such studies.
Although we did not include evidence from non-peerreviewed sources, we did write to experts in the field,
identified by consensus amongst the research team,
HSE advisors and members of our expert panel. We
asked these experts to identify any material of their own
or others’ – published or unpublished – relevant to our
research questions. We extend out thanks to the
following who responded to our request:
Prof Cary Cooper CBE
Dr Jan de Jonge
Prof Stanislav Kasl
Dr Katherine Parkes
Prof Roy Payne
Prof Dr Wilmar Schaufeli
Prof Paul Spector
Dr Jukka Vuori
Prof Peter Warr
Prof Jerry Wofford
Where experts identified peer-reviewed studies that met
our inclusion criteria, they were included in the
analysis. Other contributions were examined to inform
the discussion of theoretical paths linking individual
differences to cognitive and perceptual factors in work­
related stress.
It was the anticipated size of the literature that limited
our search to the five years 1996-2001 inclusive, so that
a sufficient number of studies could be reviewed within
the time available. We chose these years for two
reasons.
First, more recent studies are likely to have higher
standards of theoretical, methodological and analytic
sophistication and rigour – especially after the
criticisms of stress research published in the late 1980s
and early 1990s. Indeed, Zapf et al (1996) published a
paper summarizing longitudinal studies of work-related
stress. In their critique of this research, they outlined a
number of design strategies for improving longitudinal
studies to the standards where causal inference could be
reasonably drawn. In their review, they noted that very
few studies then published actually met these standards.
Therefore, we can reasonably expect to find better and
23
more rigorous longitudinal designs published after
1996.
Second, evidence indicates the presence of societal
shifts in the reporting of stress-related symptoms from
the 1950s (Twenge, 2000). By limiting the review to
more recent studies, we also limit the confounding due
to these societal shifts and ensure the findings are more
relevant to contemporary UK society.
3.1.1. Inclusion criteria
In addition to peer reviewed articles published 1996­
2001 that included measures of health, individual
differences and workplace psychosocial hazards, studies
were included in the review if they met the following
criteria:
a) Longitudinal data collection, where the independent
measures (i.e. individual differences, psychosocial
hazards) were collected before the dependent
measures, and lagged measures of the dependent
measures were taken concurrently with the
independent measures. Longitudinal data is
necessary to demonstrate causation (see e.g. Rick et
al, 2002) 7, since it is necessary to show that cause
precedes effect at the very minimum. By including
only those studies that control for initial values of
the dependent variables, it is possible to relate
changes in the dependent variables to individual
differences and psychosocial hazards, thus
strengthening our ability to say that relationships
observed are causal (Cook and Campbell, 1976;
Zapf et al, 1996).
b) Data collected from working samples, an obvious
requirement to assess psychosocial hazards in the
workplace.
c) Samples without any specific illness, either true
prospective designs where participants are screened
for premorbid signs of the illness under
investigation or ‘slice-of-life’ designs, in which
participants with non-specific illnesses might be
included (Kasl, 1983). An exception to this rule
7
This criterion is especially important as reciprocal relationships exist amongst
psychological and physical health and many individual differences (such as locus
of control, e.g. Daniels and Guppy, 1997). Cross-sectional studies are therefore
unsuitable for this review, since in the meta-analysis, we are trying to place a
statistical estimate of an effect size on a causal relationship.
24
was made for studies that were investigating illness
as a cause of further ill-health (where initial
measures of illness were treated as an individual
difference).
3.1.1.1. The search terms
Consultation with the HSE advisors and our expert
panel, through a formal meeting and exchange of email
correspondence was used to identify the search terms
for the review.
The formal meeting and subsequent email
correspondence were subject to a basic Requirements
Capture/Knowledge Elicitation exercise at the early
stages of the research project. This exercise involved
recording the initial meeting with audio equipment and
the subsequent transcription of the tapes. This
Requirements Capture/Knowledge Elicitation was
conducted in order to serve three purposes for the
review;
i) it was conducted so that it would aid the
research team in more quickly and easily
formalising the research questions and criteria
for the review process and allow the
identification of the key factors that would guide
the process;
ii)
this exercise also served to allow for a reference
point to be established that guided subsequent
discussions about the review, additions to the
review process and allowed for the ‘progress’ of
the review to be evaluated against a single
reference point;
iii)
The ‘requirements capture’ also allowed the
research team to identify quickly knowledge
gained from previous projects, lessons learned,
skills and experience acquired, as well as
methodologies and materials that could easily be
re-used during the review process. This enabled
the work to proceed efficiently and also ensured
that the ‘wheel was not reinvented’.
The search terms for the review are shown in table 3.3.
Given the inclusion criteria noted above, data-bases
were scanned for studies that included measures of
individual differences, psychosocial hazards, health and
that met the inclusion criteria. Consequently, the search
terms included ‘AND’ qualifiers for methods, sampling
25
and health. ‘OR’ qualifiers were also used to pick up
several terms potentially relevant the purposes of the
review or the inclusion criteria set.
The methods keywords were (longitudinal OR diary
OR objective_measures OR quasi-experiment OR
intervention OR experiment).
The sampling keywords were (occupational OR
organisational OR organizational OR industrial OR
work).
The health keywords were (stress OR well-being OR
health OR illness OR emotions OR affect OR mood OR
labelling).
3.1.2. The scale of the literature and sifting the
literature
Table 3.3. also shows the number of ‘hits’ for each
search. Excluding the results of the manual search, this
gave us over 4000 abstracts to sift. It was therefore
decided to engage in a two-stage sifting process (cf.
Rick et al, 2002). The first sift was designed to reduce
the total number of abstracts to a manageable number.
At this stage, the goal was to exclude papers that
definitely did not meet the inclusion criteria. Abstracts
were sifted according to the following criteria:
a) Abstract indicated the design was longitudinal or
experimental.
b) Abstract indicated the sample was a working
sample.
c) Abstract indicated the study has taken measures of
individual differences AND psychosocial
hazards/stressors AND some health type outcome
(including absence).
26
Table 3.1. Search terms used to identify abstracts.
Individual differences keywords
individual differences
No. of ‘hits’
on Web of
Science
45
No. of ‘hits’
on PsychInfo
No. of ‘hits’
on MedLine
63
12
personality
143
259
49
(Big 5 OR extraversion OR
neuroticism OR
openess_to_experience OR
conscientiousness OR agreeableness)
18
15
6
(Trait_affect OR negative_affectivity
OR positive_affectivity OR
pessimism OR optimism)
47
27
4
(Attitudes OR job-involvement OR
work_involvement OR
organizational_commitment OR
organisational commitment
284
549
93
(Dysfunctional attitudes OR
depression OR anxiety)
402
511
183
Hypochondria OR hypochondriasis
OR self_focused_attention OR
health_anxiety
500
2
0
1
0
0
(locus_of_control OR
perceived_control OR self-efficay OR
self_efficacy OR sense_of_coherence
OR hardiness)
408
0
0
(coping_style OR coping_disposition)
9
0
0
(self-esteem OR self_esteem)
78
133
42
(workability OR ability_to_relax)
2
0
0
(morningness OR eveningness)
4
0
0
symptom_amplification
0
0
0
flow
324
16
61
Total
2265
1575
450
Grand total
4290
(Type_A-Behavior_Pattern OR
Type_A-Behaviour_Pattern)
27
According to these pre-sift criteria, abstracts were
sorted as follows:
a) Include;
b) Full paper required before a decision can be made;
c) Need to discuss abstract with another member of the
review team;
d) Paper is potentially useful for the theoretical review;
e) Reject.
Abstracts falling into the first two categories were taken
forward to the next stage of sifting. At this stage, a
more rigorous sifting process was instigated, involving
formal recording of information on a pro forma. This
sifting pro forma is shown in Appendix A.
The pro forma was developed from templates used on
previous projects by members of this research team
(Rick, Briner, Daniels, Perryman and Guppy, 2001,
Rick et al, 2002). The pro forma was developed
through several iterations of consultation with members
of the expert panel and members of the core research
team. A final iteration included two of the team coding
a number of abstracts together to assess the suitability
of the pro forma.
The purpose of the pro forma was to ensure studies
included in the final meta-analysis met, unambiguously,
the criteria for inclusion. A secondary purpose was to
ensure decisions made were clear and auditable.
Reviewers were asked to consider eight questions.
These questions were meant to ensure that studies
selected were suitable for review. The questions were:
1) Was the paper published in a peer-reviewed journal?
2) Did the paper provide evidence on relationships
between an individual difference(s) and a health
outcome(s)?
3) Did the paper provide evidence on an individual
difference that fell into the typology of individual
differences developed for the review?
4) Was the paper an empirical paper?
28
5) Did the study use any kind of longitudinal
methodology?
6) Did the paper provide evidence on health outcomes
that fell into the typology of health outcomes
developed for the review?
7) Did the sample consist of working adults?
8) Were psychosocial hazards measured?
The paper was discarded if it failed any of the eight
criteria. However, relevant theoretical papers, review
papers or meta-analyses were kept in case they were
useful for the theoretical phase of this project. Sifting
was initially conducted on abstracts. However, if it was
not possible to determine answers to the questions from
the abstract, then the full paper was consulted.
To help ensure consistency of coding, an instruction set
was issued to all coders (see Appendix A). Further,
coders were invited to discuss, with other members of
the team, problematic issues or abstracts.
Statistical reliability checks were made on the coding.
A random sample of 80 abstracts coded by two of the
team was second coded by another member of the team.
Reliability was assessed by looking at abstracts that
were rejected against those taken forward to the next
stage of the review (accepted, discussed further, or in
need of obtaining full paper). In both cases, there was
80% agreement between coders.
Figure 3.1. shows the number of abstracts identified and
sifted at each stage of the search procedure. At the final
stage, a number of papers were excluded from the meta­
analysis, since they did not provide sufficient
information to be included in the analysis.
29
Figure 3.1. Stages in the review process.
Initial sift
Probably suitable
Possibly suitable
Unsuitable
N=298
N=251
N=3741
Second sift
Suitable
N=106
Unsuitable
N=4184
Review
Papers excluded during review
Number of papers included
3.2.
N=89
N=17 (total 19 samples)
Meta-analysis
3.2.1. A brief introduction to meta-analysis and
correlation coefficients
Meta-analysis is a statistical procedure for aggregating
results from several studies to provide an overall
estimate of the statistical relationships between two or
more variables. Its strength is its precision in
aggregating results, so that, for example, stronger
studies with larger samples can be given more weight in
the analysis.
However, all that meta-analysis can do is provide
statistical estimates. If the evidence base is not strong,
for example because all studies in an area use weak
cross-sectional methods, then the estimates from the
meta-analysis are similarly limited. It is for this reason
that we concentrated on the best available studies – i.e.
longitudinal studies that meet modern standards of
acceptable rigour. Indeed, as Hunter and Schmidt note
‘Substantive elimination of error of measurement is
vastly superior to elimination by statistical formula after
the fact’ (1990, p 122).
Also, because meta-analysis is a statistical tool, it
cannot easily incorporate information from qualitative
studies. Neither can the statistical analysis provide
reasons that explain the nature of the relationships
30
found: more conventional review procedures are needed
for this.
There are several forms of meta-analysis, each of which
assesses a different form of statistical relationship. The
form we use here is based on the work of Hunter and
Schmidt (e.g. Hunter and Schmidt, 1990), who have
developed procedures for aggregating and synthesizing
correlation coefficients, as the index of statistical
association. A correlation coefficient can vary between
–1 and +1. –1 indicates a perfect negative relationship
(i.e. as X decreases, Y always increases); 0 indicates
that there is no relationship; and 1 indicates that a
perfect positive relationship (i.e. as X increases, Y
always increases). In reality in the social sciences,
correlations approaching –1 or +1 are rare. It is more
usual to encounter correlations varying between -.50
and +.50. Cohen (1977) considers that in the social
sciences, a correlation over .10 (+ or –) can be
considered small, a correlation over .30 can be
considered medium and a correlation over .50 can be
considered large.
Here, the aim of our meta-analysis, then, is to provide
an overall correlation coefficient that summarises the
relationships across a number of studies. To do this, we
used the HLM-5 statistical programme, which includes
procedures for conducting meta-analysis (Bryk and
Raudenbush 1992). Since we are interested in
causation, and this can be better demonstrated in
longitudinal studies, the correlations we examine are
those where individual differences or psychosocial
hazards measured at one point in time are related to
health and related variables measured at a subsequent
occasion.
Initially, meta-analytic techniques were developed to
estimate correlations. However, if several correlations
are estimated, it is then possible estimate aggregate
partial correlations. Partial correlations give an index of
the strength of a relationship between two variables,
after taking into account the influence of other
variables. For example, if people with a generally
negative orientation tend to report more psychosocial
hazards at work and report more stress symptoms, any
correlation observed between psychosocial hazards and
stress symptoms might merely reflect the tendency of
some people to perceive the world and themselves more
negatively. However, using partial correlation allows
us to take into account such individual variation. So, a
31
partial correlation between psychosocial hazards and
health controlling for negative orientation would give us
a more accurate picture of the relationship between
psychosocial hazards and health. Similarly, when
assessing the relationship between individual
differences and subsequent health, a partial correlation
controlling for the effects of prior health would give us
as estimate of the relationship between individual
differences and change in health status. By using partial
correlation to examine changes in this way, much
stronger conclusions can be drawn with respect to
causality (Zapf et al, 1996).
In this research, then, we use meta-analysis to produce
several aggregate correlations, which we then use to
calculate a series of partial correlations:
a) Partial correlation between individual
differences and subsequent health
controlling for psychosocial hazards;
b) Partial correlation between individual
differences and subsequent health
controlling for prior health;
c) Partial correlation between individual
differences and subsequent health
controlling for both psychosocial hazards
and prior health;
d) Partial correlation between psychosocial
hazards and subsequent health controlling
for individual differences;
e) Partial correlation between psychosocial
hazards and subsequent health controlling
for prior health;
f) Partial correlation between psychosocial
hazards and subsequent health controlling
for individual differences and prior health;
In this way, we used partial correlations to assess
whether individual differences influenced health –
above and beyond the effects of psychosocial hazards
and prior health. We were also able to use partial
correlations to assess the influence of psychosocial
hazards on subsequent health above and beyond the
effects of individual differences and prior health.
32
Clearly, the strength of relationships can vary across
studies, due partly to random variation introduced by
sampling, but also because of systematic factors
embedded in different methods used in different studies.
Meta-analysis is able to examine whether the strength of
a correlation varies across studies with different
features.
For example, it is possible to examine the differences in
the size of a correlation between individual differences
and psychological and physical health. These
systematic features of studies are often referred as
moderators in meta-analyses.
An examination of some moderators is included in this
report. Specifically, we examined whether correlations
between individual differences and health and related
variables were altered:
i)
when objective rather than subjective measures
of health were used, since this would give us an
indication whether there is a difference between
the experience of health problems rather than
reporting of health problems;
ii) when acute rather than chronic health problems
were being assessed;
iii)
when psychological health rather than physical
health or health behaviours was assessed;
iv) when physical health rather than health or
psychological health behaviours was assessed
(NB this is a slightly different question to the
one posed in iii);
v)
when state and attitudinal individual differences
rather than trait differences are assessed;
vi)
when sample sizes increased. Larger samples
are more likely to find statistically significant
results. If there is a relationship between sample
size and the size of correlations, this might
indicate that editors are more likely to accept
studies that produce statistically significant
results, and that there could be a pool of studies
that have not been published that show only
small correlations;
vii) according to the proportion of men in the
sample;
33
viii)
according to the average age of the sample;
ix)
according to the time lag between initial
assessment of health and individual differences,
since this would give an indication of the
durability of any relationships uncovered;
x)
the response rate of the sample;
xi)
the country of origin of the sample. We
conducted two analyses here. First, we
compared findings from UK samples with
findings from all other samples. This would
give an indication of whether there is something
different occurring in the UK context. Second,
we examined findings from the ‘Anglo-Saxon’
cultures (e.g. UK, USA, Canada, Australia) with
findings from other cultures, to examine whether
there is something in the Anglo-Saxon cultural
context that influences the relationships between
individual differences and health and related
variables.
We examined the following potential moderators of the
relationships between psychosocial hazards and health
and related variables:
xii)
objective versus subjective measures of health;
xiii)
acute versus chronic health problems assessed;
xiv)
psychological health versus physical health or
health behaviours assessed;
xv)
physical health versus health or psychological
health behaviours assessed;
xvi)
self-report versus other methods used to assess
psychosocial hazards;
xvii)
sample size;
xviii) proportion of men in the sample;
xix)
average age of the sample;
xx)
time lag between initial assessment of health and
psychosocial hazards;
xxi)
response rate;
34
xxii) UK versus other countries, and Anglo-Saxon
versus other cultures.
When assessing the stability of psychological health, we
examined the following moderators:
xxiii) acute versus chronic psychological problems
assessed;
xxiv) sample size;
xxv) proportion of men in the sample;
xxvi) average age of the sample;
xxvii) time lag between initial assessment of health and
psychosocial hazards;
xxviii) response rate;
xxix) UK versus other countries, and Anglo-Saxon
versus other cultures.
Psychosocial measurement is plagued by problems with
reliability of measures. Even though we chose to
include only those studies that met the highest possible
methodological standards in the area of work-related
stress, measurements included in the meta-analysis
would contain some degree of error in their
measurements.
It has been long established that random error in
measurement serves to reduce the size of correlations
observed in any set of data (Hunter and Schmidt, 1990).
However, it is possible to estimate the degree of error in
well-designed measurements and it is common practice
in many journals to report estimates of the degree of
error in any given measurement by providing indices of
reliability. From these estimates, meta-analytic
procedures are then able to produce an estimate of the
size of the correlation that would be observed if all
measurements were free from random error. Estimates
of this nature are included in this report, based on the
formula provided by Hunter and Schmidt (1990).8
8
Meta-analytic procedures can also provide estimates for situations where it is
suspected that members of the sample vary over only a limited range of the
variables under consideration. These techniques were developed for selection
tools, since it is reasonable to suppose that the poorest performers would never
have been selected in the first place. However, in studies of stress, there is no
35
Meta-analysis might produce more accurate estimates of
correlations than single studies, yet they are still based
on finite, even if large, aggregated samples. This means
that the estimates produced might be inaccurate and
could vary over a range of values.
To assess this, confidence intervals can be calculated.
These provide a range of values within which the
correlation is likely to fall, and where we can be, say,
95% confident that the value calculated will fall within
that range.
Confidence intervals are especially useful where
correlations are small. If the confidence interval
includes 0, then it is possible that no relationship exists
between the variables at all. Confidence intervals were
calculated using the formula provided by Hunter and
Schmidt (1990).
As noted above, it is possible that journal editors and
reviewers are favourably biased toward studies that
show positive results. We consider that this is not a
problem where only very high quality studies are
included in the analysis, since such high quality studies
are currently rare in the work stress area, and we would
expect high quality studies to be published regardless of
whether findings indicate relationships or not.
Nevertheless, meta-analytic techniques can provide
estimates of the number of studies that would need to be
conducted to bring the estimated correlation to below
some pre-specified level. Again, we made these
estimations using the formula by Hunter and Schmidt
(1990).
3.2.2. Coding the papers
To answer the research questions set and produce
corrected estimates and partial correlations, we needed
longitudinal papers that included:
a) An estimate of the reliability of measures used in
the study;
b) Details on bivariate associations between measures
of hazards, individual differences, baseline health
reason to suppose that samples would be restricted with respect to the range of
ordinary human experience, and so the effect of range restriction was not assessed.
36
and subsequent health, so that partial correlations
could be estimated;9
c) Details on moderators.
Upon reading the papers included in the review until
this stage, it was clear that many did not provide
sufficient information to be included in the meta­
analysis. Some 66 papers did not include sufficient
information and were therefore rejected.
A further 18 papers were rejected because close
examination of the paper revealed the paper failed the
initial sort criteria.
A further five papers were excluded because they used
the same sample as reported in another paper.
Where more than one paper was provided from the
same sample, the paper chosen for inclusion had the
most extensive set of health-related variables. If this
criterion could not discriminate between papers, then
the paper with the most extensive set of individual
differences was chosen. If this still failed to
discriminate, the paper that used the largest proportion
of the sample to calculate correlations was chosen.
From the 106 papers that made it through the final sift,
only 17 studies provided sufficient information or were
sufficiently well designed to be included in the meta­
analysis. Of these 17 studies, two included separate
correlation matrices for male and female participants,
but did not include a combined matrix. Therefore, each
of these studies was treated as if it was a separate study
and the correlations entered separately into the data­
base.
Since each study was longitudinal, we analysed
correlations between health and related variables
assessed at the first wave of data collection and the
second wave of data collection and individual
differences and psychosocial hazards assessed at the
first wave of data collection.
We also analysed correlations between individual
differences and psychosocial hazards assessed at the
9
Formulae exist for estimating Pearson correlations from other indices of
association, such as odd-ratios. However, in the current review, the only papers
that included sufficient information on bivariate associations between all variables
of interest did so by reporting Pearson correlation coefficients.
37
first wave of data collection, and we analysed
correlations between health and related variables
assessed at the first and second waves of data collection.
To establish confidence intervals around aggregated
correlations, it is necessary to have information on the
sample sizes of the individual studies. Since it is
common that there is incomplete information on some
variables for some participants within individual
studies, the number of observations used to calculate
different correlations can vary within any given study.
Therefore, where information was not given on the
exact number of study participants used to calculate a
correlation, the median number used to correlate all
correlations in a study was used.
Health and related variables were coded as:
a) chronic or acute;
b) psychological, physical or a health behaviour;
c) measured objectively or subjectively.
Individual differences were coded so that high scores
represented a negative orientation, and were further
coded as trait, attitudinal or state.
All individual difference variables were assessed by
self-report in the studies that we examined.
Psychosocial hazards were coded as:
a) measured objectively or subjectively;
b) demands, control, support or other kind of hazard.
Two of the report’s authors coded independently a
random sample of 11 papers, so that 20 health and
related outcomes were coded, 20 individual differences
were coded and 20 psychosocial hazards were coded.
Across all ratings, a total inter-rater agreement of 88%
was obtained, ranging from 75% (for whether health
and related variables were chronic or acute) to 100%
(for how health and related variables were measured).
All of the papers were coded by one individual for the
meta-analysis.
38
3.2.3. Findings
The 19 studies included in the meta-analysis yielded a
combined sample size of 6087.
The mean sample size was 320.4 (standard deviation =
204.6, range 87-940).
On average, men comprised 44% of each sample
(standard deviation = 32%).
The average age of the samples was 36.1 years
(standard deviation = 7.9).
The average lag between waves of data collection was
14.6 months (standard deviation = 17.7).
The average response rate was 58% at the first wave of
data collection (standard deviation = 25%).
Two of the studies used UK samples, four used US
samples and one was an Australian sample. Ten studies
were conducted on mainland Europe and two were
conducted in Israel (these were male and female
samples reported in the same paper).
Most of the studies used self-report assessments alone
(14), four used a mix of self-report methods and other
objective measures and one was an intervention study.
All measures of individual differences were self-report.
Eighteen out of 99 correlations between individual
differences and health used objective measures of health
and related variables. Eighty-one used self-report
measures of health and related variables. Thirty-three
involved measures of acute health problems and 66
involved measures of chronic health problems. Twenty­
six involved indices of physical health, 63 involved
indices of psychological health and ten involved indices
of health behaviours. Twenty-two of the correlations
involved trait individual differences, 75 involved
attitudes and just two involved psychological states.
Twenty-four out of 120 correlations between
psychosocial hazards and health used objective
measures of health and related variables. Ninety-six
used self-report measures of health and related
variables. Thirty-six correlations involved measures of
acute health problems and 84 involved measures of
chronic health problems. Sixteen involved indices of
physical health, 88 involved indices of psychological
health and 16 involved indices of health behaviours.
Only four correlations out of 120 involved objective
measures of psychosocial hazards, the rest using
subjective methods. Forty-nine correlations involved
measures of demands, 15 measures of control, 37
39
measures of support and 19 measures of other
psychosocial hazards, such as job security and pay
equity.
For assessing stability in psychological health, 24
correlations involved measures of chronic psychological
problems and only three assessed acute psychological
problems.
3.2.3.1. Individual differences and ill-health
RQ1) To what extent do individual differences have an
influence on subsequent health-related variables in
working samples?
The raw correlations between individual differences and
subsequent ill-health ranged between –0.14 and 0.56.
Figure 3.2. shows the distribution of these correlations.
Figure 3.2. Distribution of correlations between individual differences and subsequent
ill-health.
30
20
Frequency
10
0
-.13
-.06
0.00
.06
.13
.19
.25
.31
.38
.44
.50
.56
The overall combined correlation between individual
differences and subsequent ill-health was 0.22 (95%
confidence interval 0.20-0.24).
After correcting the correlation for error of
measurement, the correlation was found to be 0.25
(95% confidence interval 0.22-0.27).
40
This indicates that a negative orientation is associated to
a small to medium degree with measures of subsequent
poor health or unhealthy behaviours.
Analysis indicates that at least another 120 longitudinal
studies showing minimal effects would need to be
conducted to bring this correlation down to a level that
would indicate there is no statistically reliable
association.10 Since Zapf et al were able to find less
than 50 longitudinal studies in the review of the stress
literature up to 1996, then it is unlikely that there are
120 studies showing minimal effects that have not been
published in recent years. Therefore, we can be
confident that our findings represent a statistically
reliable association.
RQ2) Which of trait, attitudinal or state individual
differences has the strongest influence on health-related
variables in working samples?
Because of the small number of studies looking at state
individual differences, we analysed the differences
between states and attitudes on the one hand and traits
on the other. However, we found that the difference
between correlations was very small (difference = .02,
with states and attitudes having the slightly larger
correlation with health and related variables). This
difference was not statistically reliable.11
RQ3) How do individual differences influence the
reporting, experience and expression of health?
To answer this question, we examined a range of
moderators. The majority indicated either no effects of
the moderator variables or only small differences that
were not statistically reliable.
However, two interesting and statistically reliable
differences did emerge. First, there was a large
difference between the correlation for self-reports of
health and related behaviours and objective measures.
This was a difference of 0.21 in the correlations. For
objective measures, the correlation was a very small
0.06. For self-report measures, the correlation was
10
11
We judged the cut-off for an association to be statistically unreliable as 0.03, which
is the maximum correlation in the 95% confidence interval around zero for the
combined sample in the current meta-analysis.
The conventional statistical probability of p<.05 is used to detect a statistically
reliable difference in these moderator tests.
41
0.27. Note however, that psychological health was only
assessed by self-report in these studies.
Second, there was a statistically reliable difference of
0.09 in the correlations between psychological health
(correlation = 0.23) and physical health and health
behaviours (correlation = 0.16).
These findings indicate that individual differences
might influence the experience of stress symptoms –
both psychological and physical - and related emotional
states. In turn, it is possible that this might then
influence the expression of symptoms to physicians.
However, the results indicate that individual differences
have a minimal influence on physical symptoms and
health behaviours, such as absence.
3.2.3.2. Hazards and ill-health
RQ4) To what extent do workplace psychosocial
hazards have an influence on subsequent health-related
variables?
The raw correlations between psychosocial hazards and
subsequent ill-health ranged between –0.07 and 0.64.
Figure 3.3. shows the distribution of these correlations.
Figure 3.3. Distribution of correlations between psychosocial hazards and subsequent
ill-health.
20
Frequency
10
0
-.06
0.00
.06
.13
.19
42
.25
.31
.38
.44
.50
.56
.63
The combined correlation between psychosocial
hazards and subsequent ill-health was 0.18 (95%
confidence interval 0.16-0.20).
After correcting the correlation for error of
measurement, the correlation was found to be 0.20
(95% confidence interval 0.18-0.22).
This indicates that greater psychosocial hazards are
associated to a small degree with measures of
subsequent poor health or unhealthy behaviours.
Using the same criteria as for individual differences, we
calculate that at least another 95 longitudinal studies
showing minimal effects would be needed to reduce this
association to a level that is not statistically reliable. In
the context of the number of longitudinal studies in this
area, then, we can be confident that there is a
statistically reliable association between psychosocial
hazards and subsequent health. Further, this correlation
is of a similar magnitude to findings reported by Cass,
Faragher and Cooper (in press) in their meta-analysis of
cross-sectional associations between hazards such as
low job control and indices of psychological and
physical health.
RQ5) What are some of the influences on relationships
between health-related variables and psychosocial
hazards?
As with individual differences, many of the moderators
that we examined had either no effects or effects that
were so small that they were not statistically reliable.
This includes examining the moderating effects of
different types of hazards.
There were three effects worth noting, two of which
were statistically reliable.
First, there was a statistically reliable difference
between psychological health on the one hand and
physical health and health related behaviours on the
other (difference = .11). The correlations were .22 and
.11 respectively. There was a larger statistically reliable
difference between physical health on one hand and
psychological health and health behaviours on the other
(difference = .17). The correlations were .04 and .21
respectively. Together, these results indicate that
psychosocial hazards might have the strongest effect on
psychological health, a weaker influence on health
43
behaviours and the weakest influence on physical
health.
We did not find a large difference between correlations
using self-report measures of hazards and those using
more objective measures (difference = .04). Although
not statistically reliable, self-report methods did
produce higher correlations (.22). This small difference
might reflect the small number of longitudinal studies
that used objective measures in our sample, and so no
firm conclusions can be drawn from it.
3.2.3.3. Stability in health measures
RQ6) How stable is psychological health?
The raw correlations between initial and subsequent
psychological health ranged between 0.19 and 0.82.
Figure 3.4. shows the distribution of these correlations.
Figure 3.4. Distribution of correlations between initial and subsequent psychological
health.
10
8
6
Frequency
4
2
0
.19
.25
.31
.38
.44
.50
.56
.63
.69
.75
.81
The meta-analysis indicated a large combined
correlation of 0.59 between indices of psychological
health measured at two points in time (95% confidence
interval = .57-.61).
Corrected for error in measurement, this rose to 0.72
(95% confidence interval = .69-.73).
Our analysis indicates that at least another 354 studies
would be needed with null results to reduce this
44
association to a level that is not statistically reliable.
Therefore, we can be confident in asserting that there is
a high degree of stability in negative orientation to the
work environment.
This figure of 0.59 is higher than the figure reported by
Dormann and Zapf (2001) in their meta-analysis of
stability in indices of job satisfaction (0.42). This might
be because their analysis was focused just on
satisfaction, and we included a wider range of variables.
However, the time lags between measurements ranged
from a few weeks to over ten years in the Dormann and
Zapf study. Examining those correlations produced by
Dormann and Zapf for periods of a few weeks to five
years (the range of time lags in our sample) revealed
more correlations in the range of 0.50 to 0.90. Also, our
meta-analysis examined people that stayed in the same
job. Dormann and Zapf found that the stability of job
satisfaction dropped to 0.18 amongst people that change
jobs. Combining both sets of analyses then, it appears
that a negative orientation to the environment might be
fairly stable over periods up to five years for people
staying in the same job, but it can change over longer
periods or if the characteristics of work change.
Examination of various moderators indicated no
statistically reliable differences across the moderator
variables.
3.2.3.4. Partial correlations
RQ1) To what extent do individual differences have an
influence on health-related variables in working
samples?
The partial correlations between individual differences
and health and related outcomes were found to be:
a) .20 (95% confidence interval .18-.22) when
controlling for psychosocial hazards;
b) .13 (95% confidence interval .11-.15) when
controlling for prior health;
c) .12 (95% confidence interval .10-.14) when
controlling for psychosocial hazards and prior
health.
Note - the range of the 95% confidence intervals did not
include 0, and so we can be fairly confident in asserting
that there is a causal relationship between a
45
psychologically negative orientation and measures of
health and related outcomes.
As prior moderator analyses revealed, these analyses
indicate that negative orientation is most likely to exert
a causal relationship on the psychological states and the
experience of symptoms, rather than the actual
occurrence of physical symptoms.
RQ4) To what extent do workplace psychosocial
hazards have an influence on health-related variables?
The partial correlations between psychosocial hazards
and health and related outcomes were found to be:
d) .15 (95% confidence interval .13-.17) when
controlling for individual differences;
e) .08 (95% confidence interval .07-.09) when
controlling for prior health;
f) .06 (95% confidence interval .03-.09) when
controlling for individual differences and prior
health.
Again, the 95% confidence intervals did not include 0,
and so, again, we can be fairly confident in asserting
that there is a causal relationship between psychosocial
hazards in the work place and measures of health and
related outcomes. As the moderator analyses indicated,
any causal effect is most likely to be noticed for
psychological health, followed by health behaviours
(including absence). The moderator analyses indicate
any direct influence on physical health might be
minimal.
3.3.
Summary and implications of the meta-analysis
This meta-analysis sought answers to six questions. The
analysis indicates that individual differences are
associated with subsequent changes in health and
related behaviours (RQ1), although the nature of the
individual difference (state/attitude vs trait) did not alter
the relationship between individual differences and
subsequent health (RQ2).
We did find that individual differences were more likely
to influence the psychological experience of stress
symptoms – emotional and physical, rather than the
reality of physical health (RQ3).
46
We also found that psychosocial hazards are associated
with subsequent changes in health and related
behaviours (RQ4), although there were no distinctions
between different psychosocial hazards.
Nevertheless, we found that psychosocial hazards might
exert their strongest influence on psychological health
but their weakest influence on physical health (RQ5).
Finally, we found a great deal of stability in
psychological health (RQ6).
The main findings of the meta-analysis are summarised
in table 3.2.
Table 3.2. Summary of main meta-correlations and confidence intervals.
Analysis
Meta-correlation
95% confidence
interval
Zero-order correlation of psychosocial
hazards with subsequent ill-health.
0.18
0.16 - 0.20
Partial correlation of psychosocial hazards
with subsequent ill-health, controlling for
baseline health and negative orientation.
0.06
0.03 - 0.09
Zero-order correlation of negative orientation
with subsequent ill-health
0.22
0.20 - 0.24
Zero-order correlation of negative orientation
with subsequent ill-health, controlling for
baseline health and psychosocial hazards.
0.12
0.10 - 0.14
Stability of psychological health (zero-order
correlation of baseline psychological health
with subsequent psychological health).
0.59
0.57 - 0.61
By focusing on the best possible longitudinal evidence
and controlling for initial health status, the partial
correlations derived in this meta-analysis provide some
of the strongest evidence that psychosocial hazards
cause changes in measurements of health and related
variables. Note that whilst we can be more confident
psychosocial hazards play a causal role, we can not be
sure that our measures of health really do assess health
accurately.
Nevertheless, even controlling for the influence of
individual differences, there remained a statistically
47
reliable association between psychosocial hazards and
health and related variables. Since individual
differences might influence the reporting of stress
symptoms, then we can be more confident of a true
causal relationship between psychosocial hazards and
health. In this respect, the moderator analyses are
illuminating.
Individual differences have a minimal influence on
physical symptoms and health behaviours, such as
absence. Therefore, it may be concluded that individual
differences have their strongest influence on the
experience of psychological symptoms and the
experience of physical symptoms. Psychological
symptoms are inherently experiential, and many
psychiatric disorders – especially the emotional
disorders linked most closely to stress, such as anxiety
and depression – are diagnosed by considering these
experiential factors (Lindsay and Powell, 1994). In this
case, our results suggest that self-report monitoring
systems for psychological health and psychosocial
hazards might be contaminated by some individuals
being predisposed to experience psychological
symptoms. However, this is complicated by theoretical
accounts that indicate psychosocial hazards and
individual differences operate in tandem to alter
psychological health – not independently (see chapter
5).
For physical symptoms, the situation might be a little
clearer cut. Because our results indicate that self-reports
of physical symptoms are more closely related to
individual differences than other measures of physical
symptoms, then individual differences might influence
the perception of symptoms, rather than necessarily any
underlying physical problem. Therefore, our results
suggest that monitoring systems reliant on individual
self-reports of physical symptoms might be inaccurate
and accentuated amongst those with a negative
orientation to the work environment. Even so, it might
be the case that the cognitive experience of symptoms
leads to expression of those symptoms to practitioners.
Whilst we were not able to assess the accuracy of
physicians’ diagnoses in this meta-analysis, in chapter 5
we discuss how a generally negative orientation might
influence how physicians diagnose and therefore report
illness.
Psychosocial hazards were most strongly related to
psychological health, and least strongly related to
48
physical health. The time lag in the studies was
generally small (maximum of five years). This may
have been insufficient to detect large variation in
physical health which longer term studies might be
capable of doing. However, it may be that this
differentiation of relationships indicates something of
the nature of any relationship between psychosocial
hazards and physical health. That is, the influence of
psychosocial hazards on physical health might be
mediated by changes in emotions. In chapter 5, we
revisit this idea.
However, these results do suggest that monitoring
systems are likely to be more sensitive to changes in
psychological health than physical health following
psychosocial interventions. Given strong connexions
between psychological health and individual
differences, however, our results might suggest that
monitoring systems, such as surveys, also include
assessment of negative orientations.
Even if psychological health is more closely linked to
psychosocial hazards, our results indicate that
psychological health itself, and by extension the
dynamic components of negative orientation, is quite
stable. Our analysis did not include assessment of
stability of trait negative orientation. It is likely that
trait negative orientation is extremely stable in adults,
and the trait components place limits on the extent to
which negative orientation can change. However,
Dormann and Zapf’s meta-analysis of job satisfaction
(2001) does indicate that there can be large changes job
satisfaction when the nature of work changes. In
chapter 5, we consider the processes by which
psychological health may change and the processes
through which it remains stable.
It is worth commenting on the size of our final sample.
Although we identified over 100 papers as potentially
very relevant to our analysis, only 17 studies were
sufficiently well reported to be included in the meta­
analysis. However, our estimates are consonant with
those from other meta-analytic review studies, and
calculations indicate a large number of undetected
studies with null findings would need to be conducted
to falsify our results. Further, by concentrating on the
best evidence that meets contemporary standards of
rigour for longitudinal studies (cf. Zapf et al, 1996), our
meta-analysis was able to afford much stronger
conclusions with respect to causation than had we
49
reviewed evidence from the much larger number of
studies using weaker designs.
Finally, it is worth noting that although our meta­
analysis does indicate that psychosocial hazards and
individual differences are both related to subsequent
indices of health, the meta-analysis does not indicate
how they might influence health. That is, on the basis
of the statistical findings, we cannot be sure whether
individual differences and psychosocial hazards
influence health through two or more separate causal
processes, or whether individual differences and
psychosocial hazards combine to influence the causal
processes underlying health. We turn to this question in
chapter 5.
Nevertheless, having established the importance of
negative orientation with respect to the experience and
reporting of stress symptoms, in the next chapter we
outline analyses to detect whether certain groups in the
UK population are more likely to exhibit this
orientation.
50
4. Secondary analysis of existing databases
The purpose of the secondary data analysis was to
establish the extent to which negative orientation could
be identified in certain groups in the UK population
based on demographic and biographic characteristics.
4.1. Data-bases
A number of data-bases were reviewed and two offered
a good range of variables in relation to the research
questions and were available for use in this research.
The analyses were undertaken on these two data-bases the Health Survey for England 2000 (HSfE) and the
Health and Lifestyle Survey 1991/2 (HALS2). Sample
descriptions are given here.
4.1.1. Health Survey for England 2000 (HSfE)
The Health Survey for England (HSfE) is part of a
wider programme of surveys commissioned by the
Department of Health, and is designed to monitor trends
in the nation's health. The aims of the Health Survey
series are to:
1) provide annual data about the nation's health;
2) estimate the proportion with specified health
conditions;
3) estimate the prevalence of risk factors associated
with these conditions;
4) examine differences between population subgroups;
5) assess the frequency with which combinations of
risk factors occur;
6) monitor progress towards two Health of the Nation
targets relating to blood pressure and obesity, and
7) since 1995, to measure the height of children (aged
two and over) at different ages, replacing the
National Study of Health and Growth.
The sample for the 2000 survey was Adults (aged 16
and over) and children (aged 2-15 Years) living in
private households. There was also a separate sample of
51
older people (aged 65 and over) resident in care homes
in England in the year 2000.
The 2000 HSfE consisted of two samples. The general
population sample was a national cross-sectional
sample. Up to two children aged 2-15 years were
interviewed in each household, as well as up to 10
adults aged 16 and over. All private households in the
general population sample were eligible for inclusion in
the survey.
Over 5900 adults were suitable for the analyses reported
here.
4.1.2. Health and Lifestyle Survey 1991/2
(HALS2)
HALS2 is a follow up to the 1984/5 Health and
Lifestyle Survey. The sample is constructed of adults in
Great Britain, aged 25 or over who were surveyed in
1984/5. The original participants were randomly
surveyed and this is a follow up of survivors whether
still living at the same address or not.
The sample is a longitudinal panel/cohort sample.
Measurements included face-to-face interviews, self­
completion questionnaires, and psychological and
clinical measures.
Over 2600 adults, depending on the analyses, were
suitable for the analyses reported here.
4.1.3. Procedure
The aim of the analysis was to provide evidence of
relationships of state, attitudinal and trait negative
individual differences to gender, age ethnicity, socio­
economic status and occupational grouping.
Both data sets were first examined for variables
appropriate for the analysis. The data-bases contained
self-report material on state, attitudinal and trait
negative orientation as detailed in the table 4.1 below.
As outlined in section 2.4.1., a trait individual
difference is relatively impervious to change in adults,
and traits are often taken as indicative of aspects of
personality. Attitudes are more dynamic and are
amenable to change, although any change is usually
slow. State individual differences are very fluid and can
change very quickly.
52
Table 4.1 Trait, state and attitudinal measures in the two data bases
DATA SET
Self-Reports
HSfE
GHQ-12
HALS2
Eysenck Personality Inventory – N scale
GHQ-12
Type A Behaviour
Of these, the Eysenck Personality Inventory N scale
(EPI-N) is considered more a measure of trait negative
orientation (neuroticism). Type A behaviour is here
considered as attitudinal measures of negative
orientation.12 Type A behaviour refers to a generalised
pattern of hostility, competitiveness and impatience.
Type A behaviour can change, although it is very stable.
More fluid, and hence more closely resembling state
variables, is GHQ-12 score, which was designed as a
measure of psychiatric morbidity. Nevertheless, like
attitudinal measures, these will also reflect a large
influence of trait negative orientation.
To recap, the purpose of the analysis was to look at
whether self-reports of trait, attitudinal or state negative
orientation were higher amongst certain subgroups of
the population.13
12
HALS2 also included a measure of Locus of Control, which is also an attitudinal
indicator of negative orientation. However, the scale used was specific to health,
rather than work or a generalised attitude. More importantly, the scale was found
to be unreliable, and therefore inaccurate (Cronbach’s a coefficient of reliability =
0.50). Therefore, we decided not to use Locus of Control in our assessments.
13
Differences between groups were examined with multi-factoral analyses of
variance (ANOVAs). ANOVA examines whether there are statistically reliable
differences between different groups. All demographic factors were entered
simultaneously into each analysis, and the analyses were constrained to examine
main effects only. In this way, the multi-factoral ANOVAs are analogous to
multiple regression analyses, in which the unique effects for each demographic
variable are assessed whilst controlling for the effects of all other demographic
variables. Since we had made no predictions concerning the location of statistically
reliable differences, post hoc tests were used to look for specific differences, if
ANOVA had indicated significant variability across the different categories of a
demographic factor. The specific post hoc test we used was the least significant
difference (LSD) test. For both ANOVAs and LSD tests, statistical effects were
53
4.2. Analysis
4.2.1. Analyses from HSfE2000
Table 4.2. shows the means, standard deviations and
ranges for each demographic variable from the HSfE for
GHQ-12 scores.
Table 4.2. Descriptive statistics for GHQ-12 from HsfE2000
GHQ-12
Overall
Mean
Std Dev
Range
Alpha
Sample size
1.44
2.75
0-12
0.90
5955
Men
Mean
Std Dev
Range
Sample size
1.18
2.49
0-12
2739
Women
Mean
Std Dev
Range
Sample size
1.66
2.94
0-12
3216
judged to be reliable at the conventional probability level of p<.05 (two-tailed). For
clarity of presentation, only statistically reliable effects are reported in the text.
54
Table 4.2. Continued
GHQ-12
Age 16-24
Mean
Std Dev
Range
Sample size
1.49
2.63
0-12
802
Age 25-34
Mean
Std Dev
Range
Sample size
1.37
2.62
0-12
1357
Age 35-44
Mean
Std Dev
Range
Sample size
1.45
2.81
0-12
1523
Age 45-54
Mean
Std Dev
Range
Sample size
Mean
Std Dev
Range
Sample size
1.50
2.90
0-12
1242
1.41
2.75
0-12
1031
Ethnicity
(White)
Mean
Std Dev
Range
Sample size
1.42
2.75
0-12
5508
Ethnicity
(AfricanCaribbean)
Mean
Std Dev
Range
Sample size
1.62
2.70
0-10
105
Ethnicity
(Asian)
Mean
Std Dev
Range
Sample size
1.70
2.93
0-12
236
Ethnicity
(Other)
Mean
Std Dev
Range
Sample size
1.51
2.81
0-12
104
Age 55-64
55
Table 4.2. Continued
GHQ-12
14
SES 114
Mean
(Professional) Std Dev
Range
Sample size
1.11
2.44
0-12
263
SES 2
Mean
(Managerial, Std Dev
technical)
Range
Sample size
1.32
2.53
0-12
1660
SES 3
Mean
(Skilled non- Std Dev
manual)
Range
Sample size
1.49
2.83
012
1485
SES 4
(Skilled
manual)
Mean
Std Dev
Range
Sample size
1.22
2.60
0-12
1031
SES 5
Mean
(Semi-skilled Std Dev
manual)
Range
Sample size
1.78
3.09
0-12
930
SES 6
(Unskilled)
1.84
3.04
0-12
287
SES is socio-economic status.
56
Mean
Std Dev
Range
Sample size
Table 4.2. Continued
GHQ-12
15
SOC115
Mean
(Managers, Std Dev
administrators)Range
Sample size
1.16
2.35
0-12
783
SOC2
Mean
(Professional) Std Dev
Range
Sample size
1.38
2.68
0-12
549
SOC3
(Associate
professional,
technical)
Mean
Std Dev
Range
Sample size
1.34
2.48
0-12
561
SOC4
(Clerical,
secretarial)
Mean
Std Dev
Range
Sample size
1.45
2.83
0-12
1031
SOC5
(Craft)
Mean
Std Dev
Range
Sample size
1.31
2.74
0-12
671
SOC6
(Personal &
protective
service)
Mean
Std Dev
Range
Sample size
1.57
2.83
0-12
647
SOC7
(Sales)
Mean
Std Dev
Range
Sample size
1.64
2.86
0-12
455
SOC8
(Plant &
machine
operatives)
Mean
Std Dev
Range
Sample size
1.59
3.00
0-12
528
SOC9
(Other)
Mean
Std Dev
Range
Sample size
1.66
3.00
0-12
495
SOC is occupational classification.
57
Analyses indicated that there were statistically reliable
differences between men and women on GHQ-12
scores. There were also statistically reliable differences
between socio-economic classes.
Women were found to have a more state negative
orientation than men. Expressed as a percentage of the
overall range of scores, there was an average difference
between men and women than spanned 4% of the
possible range of scores.
For socio-economic status, in general, GHQ-12 score
increased amongst lower socio-economic groups,
indicating state negative orientation increases amongst
lower socio-economic groups. The highest scores were
found amongst unskilled and semi-skilled workers
compared to all other groups. Skilled non-manual
workers reported higher GHQ-12 scores than skilled
manual and professional groups. Expressed as a
percentage of the total range of scores, the average
difference between the highest and lowest scores
spanned 6% of the possible range.
4.2.2. Analyses from HALS2
Table 4.3. shows the means, standard deviations and
ranges for each demographic variable from the HALS2
for the EPI-N, Type A behaviour and GHQ-12 scores.
Table 4.3. Descriptive statistics for GHQ-12 from HALS2
EPI-N
Type A
GHQ-12
Overall
Mean
Std Dev
Range
Alpha
Sample size
9.49
5.34
0-24
0.86
2646
0.51
0.20
0-1
0.6586
2726
1.76
2.68
0-11
0.88
2712
Men
Mean
Std Dev
Range
Sample size
8.26
5.05
0-23
1218
0.53
0.20
0.04-1
1220
1.63
2.57
0-11
1226
Women
Mean
Std Dev
Range
Sample size
10.48
5.36
0-24
1515
0.50
0.19
0-1
1520
1.88
2.76
0-11
1523
58
Table 4.3. continued
EPI-N
Type A
GHQ-12
Age 25-34
Mean
Std Dev
Range
Sample size
9.92
5.61
0-24
554
0.54
0.19
0.04-0.96
555
1.88
2.72
0-11
557
Age 35-44
Mean
Std Dev
Range
Sample size
9.47
5.29
0-24
855
0.51
0.20
0-1
860
1.66
2.61
0-11
862
Age 45-54
Mean
Std Dev
Range
Sample size
9.53
5.24
0-23
819
0.52
0.20
0.04-1.0
821
1.79
2.72
0-11
823
Age : 55-64 Mean
Std Dev
Range
Sample size
8.97
5.23
0-24
505
0.47
0.19
0-1
504
1.77
2.70
0-11
507
Ethnicity
(White)
Mean
Std Dev
Range
Sample size
9.48
5.34
0-24
2686
0.51
0.20
0-1
2691
1.76
2.68
0-11
2700
Ethnicity
(AfricanCaribbean)
Mean
Std Dev
Range
Sample size
9.47
5.21
2-19
15
0.45
0.19
0.11-0.78
15
2.00
3.12
0-11
15
Ethnicity
(Asian)
Mean
Std Dev
Range
Sample size
11.65
6.17
0-21
17
0.55
0.16
0.22-0.78
19
3.00
2.47
0-9
19
Ethnicity
(Other)
Mean
Std Dev
Range
Sample size
8.67
5.01
2-19
15
0.51
0.16
0.18-0.78
15
1.33
2.02
0-5
15
59
Table 4.3. continued
EPI-N
Type A
GHQ-12
SES 1
Mean
(Professional) Std Dev
Range
Sample size
8.79
5.23
0-23
196
0.56
0.18
0.11-0.96
196
1.83
2.71
0-11
196
SES 2
(Employers,
managers)
Mean
Std Dev
Range
Sample size
9.05
5.09
0-23
636
0.56
0.19
0.04-1.00
634
1.60
2.61
0-11
639
SES 3
(Other nonmanual)
Mean
Std Dev
Range
Sample size
9.44
5.33
0-24
524
0.53
0.18
0.04-0.93
524
1.81
2.65
0-11
526
SES 4
(Skilled
manual)
Mean
Std Dev
Range
Sample size
9.57
5.35
0-23
901
0.48
0.19
0-1
906
1.73
2.64
0-11
907
SES 5
(Semi-skilled,
personal
services)
Mean
Std Dev
Range
Sample size
10.00
5.64
0-23
364
0.45
0.20
0-0.93
367
2.02
2.99
0-11
368
SES 6
(Unskilled)
Mean
Std Dev
Range
Sample size
10.84
5.26
0-23
98
0.46
0.19
0-0.89
98
1.76
2.59
0-10
98
60
Table 4.3. continued
EPI-N
Type A
GHQ-12
SOC1
Mean
(Managers, Std Dev
administrators)Range
Sample size
8.44
4.92
0-21
306
0.60
0.18
0-1
304
1.59
2.49
0-11
306
SOC2
Mean
(Professional) Std Dev
Range
Sample size
8.13
5.14
0-23
243
0.59
0.17
0.7-0.96
242
1.71
2.59
0-11
243
SOC3
(Associate
professional,
technical)
Mean
Std Dev
Range
Sample size
9.45
5.08
0-22
200
0.57
0.18
0.11-0.96
199
1.73
2.54
0-10
201
SOC4
(Clerical,
secretarial)
Mean
Std Dev
Range
Sample size
9.39
5.05
0-23
405
0.48
0.18
0.04-0.93
407
1.65
2.65
0-11
407
SOC5
(Craft)
Mean
Std Dev
Range
Sample size
8.59
5.28
0-21
335
0.52
0.19
0.04-1.00
338
1.54
2.50
0-11
339
SOC6
(Personal &
protective
service)
Mean
Std Dev
Range
Sample size
10.20
5.65
0-23
241
0.51
0.18
0.04-1.00
241
1.73
2.62
0-11
242
SOC7
(Sales)
Mean
Std Dev
Range
Sample size
10.18
5.40
0-24
201
0.50
0.19
0.04-0.89
202
1.83
2.63
0-11
202
SOC8
(Plant &
machine
operatives)
Mean
Std Dev
Range
Sample size
9.71
5.41
0-23
239
0.47
0.19
0.07-0.96
242
1.85
2.76
0-11
242
SOC9
(Other)
Mean
Std Dev
Range
Sample size
10.24
5.31
0-24
291
0.45
0.21
0-1.00
292
1.59
2.46
0-11
293
61
Gender
Analyses indicated that there were statistically reliable
differences between men and women on the EPI-N and
GHQ-12 scores. In both cases, women were found to
have a more negative orientation than men. That is, the
results indicate that women have a greater state and trait
negative orientation than men. These differences were
more pronounced for trait negative orientation.
Expressed as a percentage of the overall range of scores,
the average difference between men and women on the
trait measure spanned nearly 10% of the possible range,
compared to a 2% difference for the state measure on
HALS2 and 4% on HSfE. It is possible that the
observed differences between genders on state negative
orientation, for both HALS2 and HSfE, reflect
differences caused by trait negative orientation.
Type A results
There were also statistically reliable differences
between age groups, socio-economic status and
occupational groups on Type A scores.
Age
For age, the results indicate a general decline in Type A
score for higher age groups, although there is not much
change through middle age. Expressed as a percentage
of the overall range of scores, the average difference
between the youngest and oldest groups spanned 6% of
the possible range.
There was a difference in scores between the 24-34
years age range and the 35-44 years age range. There
was no difference between 35-44 years age range and
45-54 years are range, although the 45-54 year age
range evidence higher scores than the 55-64 years age
range.
The results indicate that the highest attitudinal negative
orientation may occur amongst the youngest members
of the work force, and the least negative attitudinal
negative orientation amongst the oldest members of the
work force. It is not clear whether this decline is a
generalisable developmental effect or a peculiarity of
the historical circumstances in the UK at the time of the
survey.
62
Socio-economic status
For socio-economic status, the highest type A scores
were found amongst the higher socio-economic groups.
Expressed as a percentage of the overall range of scores,
the average difference between the highest and lowest
socio-economic groups was over 10% of the possible
range of scores.
Professional and managerial groups scored higher than
other non-manual groups. In turn, these three groups
had higher scores than skilled, semi-skilled and
unskilled manual groups.
Mirroring socio-economic class, the highest scores on
the Type A scale were for managerial/administrative
occupations and the professions. There were reliable
differences too between associate professional and
technical workers compared to clerical/secretarial
workers, craft workers, personal and protective services
workers, sales workers, plant and machine operatives
and people in other occupations.
Of the remaining occupations, there were no reliable
differences between craft workers, personal and
protective services workers and sales workers, although
craft workers scored significantly higher than
clerical/secretarial workers.
Craft workers and personal and protective services
workers also reported higher levels of type A behaviour
than plant and machine operatives.
Other occupations reported significantly lower scores
than all other groups, excepting plant and machine
operatives. Expressed as a percentage of the overall
range of scores, there was around a 15% average
difference between the highest and lowest scoring
groups over the possible range of scores on the Type A
measure.
Ethnic groups
Although there were no statistically reliable differences
between ethnic groups, the small numbers in the sample
of non-White participants makes detecting reliable
statistical differences more difficult than if there were
larger numbers from different ethnic groups. Therefore,
there may be differences between ethnic groups that we
were unable to detect. However, there were larger
sample sizes for different ethnic groups in HSfE, and no
63
reliable differences emerged between ethnic groups on
GHQ-12 scores.
4.3. Summary and conclusions
The most consistent finding to emerge from the
secondary analyses is that there is a greater likelihood
of a more negative orientation in women than men. This
finding emerged for GHQ-12 scores from the HSfE and
the HALS2 data, and the EPI-N scores from the HALS2
data.
The strongest results occurred for the EPI-N scores,
indicating differences in trait negative orientation
between men and women. The weaker differences on
the more state-like GHQ-12 that were found from data
collected in the early 1990s (HALS2) and 2000 (HSfE)
might reflect differences in underlying traits. This
might suggest that differences in negative orientation
between men and women reflect relatively immutable
factors, that cannot be changed, if at all, within society
over the shorter term.
The implications of this gender difference can be drawn
out from the results of the meta-analysis. Since a
negative orientation is associated with subsequent
changes toward greater experience of psychological and
physical problems, we might expect a slight tendency
for there to be greater self-reports of psychological
distress and self-reports of physical symptoms amongst
women. In turn, this may lead to greater visits to
physicians.
This tendency, which might be reflected in both self­
report and physician monitoring systems, may remain
notwithstanding changes to work environments.
However, any tendency is likely to be slight at best,
given the relatively small differences between genders
and the strength of the relationships found in the meta­
analysis.
There did emerge some differences between age groups,
with younger members of the adult population
displaying more negative attitudes.
There was a decline in reports of negative orientation
between younger groups and the middle-aged groups,
and further differences emerged between the middle­
aged groups and the oldest groups in the adult
population of working age.
64
Consequently, we might expect there to remain a
tendency to experience and report more psychological
and physical symptoms in younger age groups16, and a
corresponding tendency to visit physicians. Again, this
might manifest itself in monitoring systems based on
self-reports or physician reports.
However, the age-related differences we found were
attitudinal. Therefore, we might expect that such
negative orientations are susceptible to slow change.
Indeed, a developmental explanation of this relationship
would imply change in orientation as individuals age.
Overall with respect to age, we can conclude that the
tendency to experience symptoms, that are not causally
attributable to the work environment, may be less
evident over time as working environments change.
However, any change in younger cohorts is likely to be
subtle, lag far behind changes in environments, and so
be observable as these groups mature. Again, any
effects related to age are likely to be slight given the
strength of the relationship found in the meta-analysis.
A mixed pattern of findings emerged with socio­
economic class and occupational groups. Attitudinal
negative orientation was found to increase with socio­
economic status and found to be generally higher
amongst professional, managerial and associate
professional/technical workers and lowest amongst
machine and plant operators and other workers in the
HALS2 survey. In contrast, state negative orientation
was found to be higher amongst lower socio-economic
groups in the HSfE. There are a number of competing
explanations for this finding.
One explanation centres on the time difference (8 years)
between the two surveys. No differences amongst
socio-economic groups were found for GHQ-12 scores
in our analyses of the HALS2 survey. However,
differences in GHQ-12 score emerged in the later HSfE.
This might indicate that working and/or other
conditions have deteriorated relative to other groups, or
at least have been perceived to deteriorate, amongst
lower socio-economic groups between the two surveys.
Such changes are more likely to be manifest for GHQ­
12 scores, since they are more reactive to changes in
conditions.
16
Although, in general, older groups might report more physical problems, since they
are more likely to develop physical disorders over time.
65
However, this explanation gives no indication why there
are differences in attitudinal negative orientation for
socio-economic groups and occupational groups in the
HALS2 survey. State, attitudinal and trait indicators of
negative orientation are certainly highly correlated at an
individual level (e.g. Wofford and Daly, 1997).
Differences between social groups might reflect
socialisation of different expectations between these
groups. Type A behaviour typically refers to ambition
and competitiveness, amongst other things such as
irritability and hostility. Ambition and competitiveness,
especially in the workplace, may be attitudes that are
valued by members of higher socio-economic groups
and managerial, professional or associate professional
occupations. Such values may be passed on in these
groups, for example through career systems that reward
behaviour driven by such values.
The hostility and irritability that accompany ambition
and competitiveness in the Type A pattern may be
reactions to expectations that exceed a person’s working
and other social conditions. In support of this, the
finding that Type A pattern is less prevalent amongst
older people might reflect the eventual achievement of
career and other life expectations (cf. Sturges, 1999).
Notwithstanding the interpretation of the pattern of
results, the influence of negative orientation on reports
of symptoms will dissipate more readily following
improvements in job design amongst lower socio­
economic groups and less skilled workers. This is
because GHQ-12 is more dynamic than Type A pattern.
Once again, however, any changes are likely to be
small, given the relatively small differences between
socio-economic and occupational groups and the
strength of the relationships found in the meta-analysis.
4.3.1. Conclusion
In conclusion, we did find differences across different
demographic groups in state, attitudinal and trait
negative orientation. Such differences may have
implications for the accuracy of monitoring systems in
different groups.
However, demographics are only weak indicators of
negative orientations, and so it might be best to assess
negative orientations directly in monitoring systems.
66
5. Discussion In chapter 3, on the basis of the meta-analysis, we
concluded that:
a) Psychosocial hazards are associated more closely
with subsequent increases in psychological
symptoms;
b) A negative orientation is associated with subsequent
increases in the cognitive experience of
psychological and physical symptoms;
c) There is a good deal of stability in psychological
health – and by implication - negative orientation
(although changes in work conditions might
attenuate this stability).
On the basis of these findings, we suggested that the
reporting of physical symptoms might be inflated for
those with a negative orientation where monitoring
systems are based on self-report. We also suggested
that monitoring systems are likely to be more sensitive
to changes in psychological health rather than physical
health, at least in the short term.
Finally, we suggested that emotions might mediate the
relationships between psychosocial hazards and
physical health, so that psychosocial hazards cause
changes in psychological factors, which subsequently
cause changes in physical factors.
We noted that the pattern of the findings do not indicate
how individual differences and psychosocial hazards
might cause changes in health and related variables.
That is, the meta-analysis does not indicate whether
psychosocial hazards and individual differences operate
alone or in tandem. In reviewing explanations of the
role of individual differences and psychosocial hazards,
it is clear that theoretical accounts indicate that the
causal paths linking work to the experience, expression
and reporting of symptoms are influenced by both
psychosocial hazards and individual differences. In the
rest of this chapter, then, we examine theoretical models
that attempt to explain:
a) how a negative orientation increases the cognitive
experience of symptoms;
b) how psychosocial hazards influence emotions;
67
c) how emotions influence reporting of symptoms and
psychosocial hazards;
d) the processes by which emotions change and also
how they remain stable;
e) links between emotions, the experience of physical
symptoms and the expression of physical symptoms
to physicians;
f) how society, the person and the environment
influence the development of emotions.
We place emotions17 at the centre of our analysis for
three main reasons:
a) Many current and influential theoretical frameworks
of the study of work-related stress accord a central
role for emotions as the link between hazards and
disease (e.g. Karasek and Theorell, 1990; Spector
and Goh, 2001);
b) Emotional distress is central to the diagnosis of
psychological disorders commonly linked to
environmental stress (i.e. depression, anxiety,
Lindsay and Powell, 1994);
c) It is well established that emotions influence
cognitive processes (Rusting, 1998), and so
emotions might influence the cognitive processes
involved in the experience of psychosocial hazards
and stress-related symptoms.
5.1.
Paths from individual differences to occupational ill-health –
theoretical overview
Spector, Zapf, Chen and Frese (2000a) have identified five
ways in which a negative orientation could influence the
experience and reporting emotional health at work. These
are:
a) differential perception of psychosocial hazards – that
is people with a negative orientation are more likely to
notice psychosocial hazards, and so are more likely to
report them;
17
Recall that ‘emotion’ refers to feelings towards an event, situation, object or person
and ‘mood’ refers to feelings that can not necessarily be linked to a specific event,
situation, object or person. ‘Affect’ is a term that subsumes both emotion and
mood.
68
b) perceptual influences of mood – that is people with a
negative orientation are more likely to experience
negative emotions, and these negative emotions then
influence the perception and impact of hazards on
health;
c) greater incidence of psychosocial hazards – that is
people with a negative orientation are more likely to
behave in ways that encourage negative events to
occur, such as getting into interpersonal conflict;
d) interaction with psychosocial hazards - that is people
with a negative orientation are more reactive to
psychosocial hazards, and so are more likely to
experience distress when confronted with a
psychosocial hazard.
e) drift into psychologically hazardous jobs – that is
people with a negative orientation do not present
themselves in the best possible light during selection
interviews and related selection processes, and so such
people are less likely to get desirable jobs and drift
into jobs with more psychosocial hazards.
These five paths identified by Spector et al indicate that
theoretical models need to explain; i) how psychosocial
hazards influence emotions (to explain c) partly and e));
how individual differences and mood influence how we
interpret emotional information (to explain a), b) and
d)), and how individual differences influence the
occurrence of psychosocial hazards (to explain c)).
In the following sections, we review four theoretical
models of how psychosocial hazards influence
emotions, namely affective events theory (AET),
appraisal theory, action-control theory and a cognitive
model. The last of these models illuminates each of the
five paths outlined by Spector et al.
5.1.1. Affective Events Theory
Affective Events Theory (Weiss and Cropanzano, 1996)
indicates:
a) work-related attitudes and behaviours are influenced
by work-related emotions;
b) work events cause work-related emotions;
c) work events are caused by more stable on-going work
conditions.
69
In other words, work environments and characteristics of
those environments influence emotions through specific
events. In a sense, this echoes suggestions made by Rick
et al (2001) that assessment of psychosocial hazards
should concentrate on measuring the frequency of
occurrence of psychosocial hazards rather than assessing
hazards using scales that indicate agreement or
disagreement with a phrase describing an individual’s
job.18
AET also indicates that it is not the event per se that
causes affective reactions, but a person’s interpretation of
that event. Indeed, the interpretation an individual gives to
an event has been shown to be important in explaining
stress-related reactions to that event (Dewe, 1989). AET
also recognises that a negative orientation can influence
that interpretation.
5.1.2. Lazarus, appraisal and coping
Appraisal based theories have considered in detail the
processes by which people come to interpret events as
stressful. The most influential appraisal theory, especially
in relation to occupational stress (Cooper et al, 2001), is
Lazarus’ theory (e.g. 1999).
Lazarus’ appraisal theory indicates that people interpret
events according to whether they have an impact on a
person’s ability to pursue his or her goals. According to
Lazarus (1991, 1999), there are two major elements of
appraisal.
a) Events are first subject to primary appraisal that
determines an initial emotional reaction. During
primary appraisal, an individual determines whether
an event is relevant for personal goals or whether the
event will enable or block pursuit of goals. These
goals can come in many forms, such as performance
targets. However, Lazarus suggests that we also have
higher-order goals, such as establishing self-esteem,
maintaining moral values and pursuing life or career
goals. Events that block these more important goals,
18
That is, Rick et al suggest psychosocial hazard scales should ask how often
something occurs (preferably in a defined time period) and ask individuals to
respond on a scale that, for example, gives response options of ‘Never’, ‘Once or
twice’, ‘Several times’, ‘Most of the time’ ‘All of the time’. Many current scales
ask respondents to indicate their agreement with statements with response options
such as ‘Strongly agree’, ‘Agree’, ‘Neither agree nor disagree’, ‘Disagree’,
‘Strongly disagree’.
70
or an individual’s plans to achieve them are, perhaps,
more likely to arouse more intense emotional
reactions.
b) Second, events are subject to secondary appraisal to
determine coping. Coping can be thought of as
attempts to adapt to or regulate the emotional impact
of an event. It can include, for example, attempts to
re-establish progress toward goals that have been
disrupted, attempts to control or vent emotions,
attempts to re-interpret the impact of the event on
goals or attempts simply to avoid thinking about the
event and its implications. If coping is successful,
then emotional well-being is restored. Indeed, earlier
versions of Lazarus’ appraisal theory also included a
component of tertiary appraisal in the model - the
evaluation of coping success (Lazarus and Folkman,
1984). However, the role of tertiary appraisal has been
played down in later versions (Lazarus, 1991, 1999).
The content of these two major elements of appraisal
combine to form ‘core-relational themes’ that determine
the specific emotion felt. For example, the emotion of
anxiety is characterised in Lazarus’ theory as related to the
core-relational theme of threat.
The notion of goal-related appraisal is intuitively
appealing, since it can account for individual differences
as different people have different goals (not all of which
accord with the formal objectives of their work). The
notions of goals and coping also indicate that people are
active in the work-related stress process, and attempt more
or less successfully to pursue personal goals and regulate
the impact of events on those goals.
Appraisal theory also indicates that paying attention to
how people pursue goals (and how these can be made
consonant with organisational objectives) might be an
effective way of promoting well-being in the workplace.
5.1.3. Action-control theory and goals
Lazarus’ appraisal theory points to the importance of
goals. Action-control theories have considered in
greater detail the cognitive processes involved in
relating goal progress to emotions. Action-control
theories have also considered the cognitive processes
involved in coping.
Carver and Scheier’s control theory (1990) proposes
that it is not merely disruption to goals that causes
71
unpleasant emotions, since if we expect to be disrupted
it does not influence our emotions. Rather, Carver and
Scheier propose that negative emotions occur when the
rate of progress toward a goal is slower than desired.
Conversely, positive emotions are proposed to occur
where the rate of progress is faster than desired.
This has important practical implications for managing
work-related stress – since procedures have to be in
place to allow individuals to pursue their goals at the
rate that they desire, or organisations could aid
individuals’ understanding of how fast they can expect
to achieve their goals so that individuals can set their
expectations accordingly.
Carver and Scheier, like Lazarus, consider goals to be
arranged hierarchically, with subordinate goals
representing stages in plans to achieve higher-order
goals. For example, the higher-order goal of ‘SPEND
MORE TIME WITH FAMILY’ might be achieved
through lower-order goals such as ‘LEAVE WORK AT
5pm’ or ‘DON’T TAKE WORK HOME’. The
importance of these lower-order goals is they include
plans that detail behaviours to achieve a higher-order
goal (Frese and Zapf, 1994).
In this sense, action-control theories link notions of
cognition (e.g. how we interpret an event with respect to
goal progress) with behaviour (e.g. what do we do about
events that disrupt goal progress). Therefore, it is
logical that people should formulate plans concerning
how to prevent psychosocial hazards occurring in the
first place and plans to enact coping behaviours if
hazards do occur.
The practical implication of this is that if there is
sufficient latitude available in the workplace to enact
these plans, then there exists the possibility of
individuals being able to regulate their own emotions.
In many theories of job design, the latitude to enact
plans for stress-regulation is embedded in the ability to
exercise discretion over work tasks, participation in
decision making and availability of social support from
managers and co-workers (cf. Karasek and Theorell,
1990).
However, the central place of goal-related appraisal as a
cause of emotions has been criticised, in that emotional
reactions to events can occur too quickly for detailed
interpretation of an event’s impact on an individual’s goals
72
(Zajonc, 1980). To address this issue, Power and
Dalgleish (1997) argued that emotions can occur in two
ways:
a) Through interpretation of an event’s implications for
goal progress, which is likely to involve detailed
conscious deliberation;
b) Through learning the kinds of events that are likely to
cause disruption to goal progress.
By learning that some events have disrupted progress to
goals in the past, people can then infer that events of the
same kind are likely to influence goal progress in the
future. This learning, then, provides a cognitive short-cut
through which people can infer more quickly that an event
has implications for goal disruption. This inference is
made on the basis of a few, key defining features of an
event to enable faster processing of information.
However, whilst this learning affords a faster
interpretation, and therefore faster mobilisation of coping,
the process is largely unconscious, very inflexible and can
involve misinterpretations of an event’s implications for
goal progress. In this way, emotional responses can seem
irrational.
As an example, one of the research team met a woman
who became very upset when her line manager raised his
voice. Although this had no implications for her work or
personal goals, and the line manager didn’t raise his voice
in an aggressive manner directed toward her, the woman
nevertheless became upset and would often cry too.
A closer examination revealed that the woman’s father
had often shouted at her when she was a child. The line
manager raising his voice reminded her of this and so she
associated older men raising their voices with her father
being angry with her. In this way, the emotion elicited by
her line manager shouting was done so automatically and
with no relevance to the current work context.
For Power and Dalgleish, it is beliefs about events that
influence our interpretation. If we believe an event has
implications for our goals, then it is more likely to cause
an emotional reaction. Power and Dalgleish suggest that
we have networks of related beliefs, stored in memory
as mental models (our own ‘lay’ theories of how the
world works – see section 2.3), and it is these networks
that influence how we respond emotionally and
behaviourally to distressing events.
73
5.1.4. CAPHEM model
Building on the notion of there being two paths to
generating emotion and that beliefs influence appraisal,
Daniels and colleagues have been developing a
cognitive model of how workplace psychosocial
hazards influence emotions, and how the work
environment and people’s beliefs interact to influence
coping behaviour (Daniels, 2001; Daniels, Harris and
Briner, 2002a; 2002b; 2002c; in press).
Daniels and colleagues have labeled their model
CAPHEM,19 which stands for
CAtegorisation of
Psychosocial
Hazards and
EMotions
There are five major elements of the CAPHEM model.
1) Categorisation. Before an event can influence
emotions, it needs to be noticed and then categorised as
emotionally relevant. Categorisation can occur in two
ways:
· First, an event can be categorised as relevant for
current goals. This starts the slow process of goal­
related appraisal described by Power and Dalgleish
(see element 2 below).
· Second, an event can be categorised a similar to
events that have disrupted goals in the past. Here,
categorisation is made on the basis of a few defining
features of an event that are similar to or the same as
events encountered in the past that have disrupted
goal progress. This type of process starts the fast
process of appraisal described by Power and
Dalgleish (see element 3 below).
2) Goal-related inferences. Categorisation of an event
can cue the recall of beliefs about that event. It is the
interaction between what is perceived in the
environment and beliefs about events of the same nature
that influence how an event is appraised as influencing
progress toward a person’s goals.
19
The bibliography for the CAPHEM model is shown in Appendix B.
74
For example, consider someone with the goal of writing
a report for a deadline. If a colleague is being
uncooperative, then this might be classified as
‘UNSUPPORTIVE COLLEAGUES’. In turn, this
classification evokes the belief that
‘UNCOOPERATIVE COLLEAGUES DELAY
TASKS’. In turn, the person might then conclude that
lack of cooperation disrupts that person’s ability to
submit the report on time and a negative emotional
reaction, such as anger, anxiety or sadness results.20
Daniels et al argue that, the more important a goal, the
stronger the emotional reaction produced by
impediments to the desired rate of goal progress.
3) Emotional associations. Categorisation of an event
through consideration of previous events of the same
kind can proceed at the same time as element 2 or
independently of element 2.
As categorisation is fast and made on the basis of only a
few key features of the event, then minimal attention is
paid to the environment, which increases the chances of
a seemingly irrational emotional reaction. If we return
to the example of the woman and her line manager, here
the woman will have noticed the defining features of
OLDER MAN and RAISED VOICE. For this woman,
the combination of these features means the event is
similar to that category of events where FATHER IS
ANGRY WITH ME, which in turn elicits the emotional
reaction the woman experienced as a child.
4) Coping. Once an event has been categorised and an
initial emotional reaction has occurred, a set of plans is
recalled concerning the best way of coping with an
event of the same nature.
Where goal-related inferences are made and more
attention is being paid to the environment, the plans are
evaluated against their suitability for the specific nature
of the current event and modified accordingly.
Where the faster unconscious processes cause emotion
and little attention is paid to the environment, then the
person tries to implement plans that might have been
successful in the past but might not be suitable for the
20
There is differentiation between emotions in the CAPHEM model. Anger is
thought to result from obstacles to goal progress; anxiety from threats to goal
progress; sadness from failure of goals or the loss of the means to achieve goals;
and boredom from events that have no implications for personal goals.
75
current situation. In this way, unconscious processes
can increase the chances of ineffective coping.
Like other approaches (e.g. Karasek and Theorell,
1990), control over work tasks and support from others
are thought to enable the implementation of coping
plans, and so promote effective coping and well-being.
5) Feedback. There is considerable evidence that
emotions influence cognition and the social processes of
coping. Specifically, if coping is ineffective and a
negative emotional reaction persists, this directs
attention to more negative features of the environment
and so increases the chances of noticing other
psychosocial hazards. Also, if a negative emotion
persists, there is an increased chance of having control
and support withdrawn, as others perceive the person to
be less competent, less capable of executing effective
control and less likely to reciprocate supportive
behaviours
Figure 5.1. illustrates the major elements of the CAPHEM
model.
76
Figure 1. Major elements of CAPHEM model.
Mental model
(adapted from Daniels et al, in press).
Coping
plans
Inference for
goal progress
Categorisation
of hazard
HAZARD
PERCEPTION
Perceived goal
progress
Coping
behaviour
Emotion
Control
&
support
Categorisation
of hazard
Past association
with emotion
Mental model
77
Coping
plans
The CAPHEM model relates to the five paths identified
by Spector et al in the following ways:
a) differential perception of psychosocial hazards: People
with a negative orientation are likely to have more
mental models of psychosocial hazards (Williams et
al, 1997), meaning that such people are more likely to
categorise events as aversive;
b) perceptual influences of mood: Because people with a
negative orientation are more likely to categorise
events as hazards, they are more likely to experience
negative emotions. In turn then, they are more likely to
notice negative aspects of the environment, leading
them to categorise events as hazardous, leading to
more negative emotions and so on;
c) greater incidence of psychosocial hazards: since a
negative orientation is more likely to cause negative
emotions, it is more likely that those with a negative
orientation will receive less support and control in the
work place. In turn, this makes these people less able
to cope with hazards, meaning that hazards can persist
for longer;
d) interaction between affectivity and psychosocial
hazards: People with a negative orientation have more
elaborate mental models of psychosocial hazards that
indicate those hazards are somehow harmful or
disruptive to personal goals. Therefore, when a hazard
is noticed, people with a high negative orientation are
more likely to infer that the hazard might disrupt goals
or has been associated with negative emotions in the
past;
e) drift into psychologically hazardous jobs: The
CAPHEM model does not give an account of how
people with a negative orientation might come to work
in more psychologically hazardous jobs. It does,
however, indicate that people who display negative
emotions might be perceived as less competent –
which could influence selection or promotion
decisions for more desirable jobs, or redundancy
decisions. The CAPHEM model does explain how
hazards in less desirable jobs are linked to emotions.
The CAPHEM model also has implications for
reporting of psychosocial hazards from questionnaires
and for how emotions change or stay the same. These
implications will be drawn out in subsequent sections.
However, the CAPHEM model does not deal with the
78
expression of symptoms or the physiology of emotions
– which is thought to influence physical health. Other
research and models will be used to explain these
processes.
5.2.
Individual differences & self-report measures: correlation
inflation
If individuals with a negative orientation are more prone
to notice psychosocial hazards, then they could be more
likely to report them. Individuals with a negative
orientation are also more likely to experience and report
psychological and physical symptoms.
Therefore, associations between psychosocial hazards
and symptoms are likely to be inflated for people with a
negative orientation – giving the impression of a false
relationship between the work environment and health
(but not perhaps necessarily a false impression of the
relationship between perceptions of work and
experience of symptoms which, as argued above, is key
for the development of emotions).
Many studies have examined the influence of negative
orientation on correlations between self-reports of work
conditions and symptoms. In particular, much attention
has been directed toward the concept of negative
affectivity – or the predisposition to experience negative
emotional states. It is clear from this stream of research
that negative orientation does indeed inflate reports of
psychosocial hazards and symptoms. Specifically,
much research in this area has found that partial
correlations controlling for negative orientation can be
substantially lower than simple correlations between
hazards and symptoms (e.g. Brief, Burke, George,
Robinson and Webster, 1988).
The meta-analysis demonstrated, however, that even
after controlling for negative orientation, we found that
psychosocial hazards still had a significant association
with changes in subsequent health status.
5.2.1. Occasion factors
Most studies of negative affectivity have been cross­
sectional. However, Zapf et al (1996) have noted that
mood might also influence perceptions of the work
environment in the same way as negative affectivity,
which is also consistent with the CAPHEM model.
Since mood is more variable than negative affectivity,
Zapf et al argue that ‘occasion factors’ might influence
79
reports of hazards and symptoms, inflating observed
associations for people in a negative emotional state at
the time of measurement.
Spector, Chen and O’Connell (2000b) went someway
toward testing whether occasion factors might influence
associations between self-reports of hazards and
symptoms. In their study, they found that associations
between hazards and symptoms were usually similar in
size to partial correlations controlling for negative
affectivity measured some months before self-reports of
hazards and symptoms.
However, there emerged significant reductions in the
size of correlations between hazards and symptoms
when partial correlations controlled for negative
affectivity measured at the same time as hazards and
symptoms. These results do suggest that occasion
factors might influence self-reports of hazards and
symptoms inflating observed associations between
hazards and symptoms measured at the same time. Such
findings underline the importance of longitudinal
research for drawing inferences about whether
psychosocial hazards cause symptoms.
5.2.2. How mood and disposition interact to
determine how we notice psychosocial hazards
It is reasonable to suppose that occasion factors and
negative affectivity interact to influence correlations
amongst self-report measures (cf. Rusting, 1998). If
people with a negative orientation are more likely to
have more elaborate mental models of psychosocial
hazards, and a negative mood directs cognitive
processes to negative information, it is reasonable to
suppose that a negative mood increases the chances of
interpreting events as hazards disproportionately for
people with a negative orientation. That is, the greatest
inflation of associations between self-reports of hazards
and symptoms occurs when people with a negative
orientation are in a negative emotional state.
Although experimental research has indirectly assessed
this proposition (see Williams et al, 1997 for a review),
we know of no studies that have addressed this
proposition directly in occupational samples.
5.3.
Change & stability in psychological distress
Although there is stability in negative orientation and
psychological health, as Spector et al (2000b) indicate,
80
even trait measures of negative orientation may be
subject to some change. Therefore, it is important to
consider how negative emotions in themselves, and as
indicators of negative orientation, are sustained and how
they change in work environments.
5.3.1. Change & stability explained in the
CAPHEM model
In the CAPHEM model, emotional change can occur in
several ways.
First, hazards can cause emotions through two primary
processes that can be simultaneous, involving
inferences concerning events’ implications for goals or
learnt associations between events and past disruption
of goals. The faster process might cause one emotional
reaction, which is subsequently modified by the slower
process. For example, you may be initially angry by
being asked to work on a project with a colleague who
has caused problems for you in the past. However, you
may become pleased when you realise that this is an
opportunity to demonstrate your managerial
competence (and promotability) by getting the best out
of this colleague.
Second, at least when making inferences about goal
progress, a fair amount of information from the
environment is used to generate inferences and decide
on coping options. Therefore, as an event proceeds and
the nature of the hazard changes, inferences about goals
too may change. This can then produce changes in
emotions.
Third, coping processes can alter emotions directly or
indirectly though changing the interpretations of
hazards or removing those hazards from the
environment.
Fourth, psychosocial hazards can occur concurrently or
sequentially and emotions influence cognitive
processes. In this instance, an emotional reaction
generated by one hazard might influence processing of
information on another hazard.
It is the influence of emotions on cognitive processes
that can explain stability in the experience of unpleasant
emotions. Here, negative orientation might play a role
in the maintenance of negative emotions by activating
well-learnt mental models (Williams et al, 1997, cf.
section 5.2.2.). Negative emotions are maintained by
81
directing information processing toward categorising
events as psychosocial hazards, which then cause
further negative emotions.
5.3.2. Small effects sizes and large changes:
vicious and virtuous circles
Although there is a great deal of stability in
psychological ill-health and negative orientation,
research does indicate that psychological health can
change and more negative orientations become more
positive (Firth-Cozens, 1992; Firth-Cozens and Hardy,
1992).
It has been proposed that such changes follow the
model of ‘vicious’ and ‘virtuous’ circles (Daniels and
Guppy, 1997; Firth-Cozens, 1992), with small scale
improvements in well-being leading to cognitive
processes being directed to positive information, in turn
leading to less attention being paid to psychosocial
hazards and establishment of new mental models that
replace or mutate older, more negative mental models.
These new mental models allow more positive
interpretations to be made, and so a negative orientation
gradually becomes more positive, reinforcing further
improvements in psychological health. Of course, the
reverse process might occur, with worsening
psychological health leading to establishment of more
negative mental models, in turn reinforcing further
deterioration of psychological health.
These processes are thought to be incremental and to
build over a period of time. The findings from two
daily diary studies reported by Harris (2002) indicate
well-being before work could predict changes in mental
models of work conditions over the course of a single
day, although no conclusions on factors that might
sustain these changes was presented. Another study
two-stage longitudinal study did indicate well-being
could predict subsequent improvements in some
perceptions of work and a lessening of attitudes closely
related to a negative orientation (Daniels and Guppy,
1997).
However, studies over periods of several months are
conflicting, with one study indicating well-being can
predict improvements in perceived work conditions
(Wolpin, Burke and Greenglass, 1991) and another
indicating no relationship between well-being and
subsequent changes in perceived work conditions (de
82
Jonge, Dormann, Janssen, Dollard, Landeweerd and
Nijhuis, 2001).
The factors that sustain or inhibit changes in perceptions
of work environments are not widely understood. It
may be that sustained changes of periods of several
months are very subtle, and require measurements
specific to occupations or organisations, or precise
measures of appraisals or mental models, rather than
more generalised measures, so that changes in
perceptions of very specific work conditions can be
detected (cf. Daniels & Guppy, 1997 or Harris, 2002,
with de Jonge et al 2001). Moreover, some perceptions,
such as those linked to personal goals, might be more
readily influenced by changes in the work environment,
whilst others are more closely linked to personality
and/or organisational culture and therefore less
amenable to change (Harris, 2002). Nevertheless, one
practical implication of these suggestions is that any
interventions might take some time to be noticed,
perhaps as long as five years or more (Dormann and
Zapf, 2001) since larger scale changes may need to
build over time. Even then, a range of other factors that
are not currently fully understood might need to be
taken into consideration in order to determine the
probability of successful intervention.
5.4.
Individual differences and the experience of physical symptoms
We have suggested that emotions play a central role in
the experience and reporting of physical symptoms as
well as psychological symptoms. There is also some
discussion of how psychosocial hazards might influence
emotions, which in turn influence physiology and
physical health (Schwartz, Pickering and Landsbergis,
1996).
5.4.1. The role of negative orientation in the
perception of symptoms
Kirmayer, Robbins and Paris (1994) have identified five
paths through which personality might influence the
cognitive experience of physical symptoms, either
without the presence of physical symptoms or
accentuating such symptoms as are present. These are:
1) Somatic perceptions – in which some individuals
are more simply more likely to notice physiological
symptoms;
83
2) Somatic attention – in which some individuals are
more likely to pay attention to bodily sensations,
and therefore become more likely to notice
symptoms that would not be noticed by others;
3) Somatic amplification – symptoms that are noticed
are blown out of proportion to be experienced as
worse than they really are (e.g. a tendency to
experience ‘bad’ headaches).
4) Difficulty distinguishing bodily sensations from
cognitive sensations – which occurs when
individuals misinterpret the cognitive experience of,
say, anxiety and associated physiological changes as
illness;
5) Tendency to catastrophise – which occurs when
common physiological symptoms are interpreted as
indicating a more serious disease (e.g. indigestion
interpreted as angina).
Research indicates a negative orientation is associated
with paths 2), 4) and 5) (Kirmayer et al, 1994), although
it is possible, of course, that a negative orientation
might influence all five paths.
That negative orientation might influence the fifth path
indicates that people with a negative orientation might
be more inclined to visit physicians, as they interpret
their symptoms as indicating serious conditions. Even
so, it is clear that there is theoretical discussion to
support our earlier conclusions that monitoring systems
that rely on self-reports of stress-related physical
symptoms might over-estimate their prevalence
amongst people with a high negative orientation.
5.4.2. The role of negative orientation in the
presentation and diagnosis of symptoms
If a negative orientation leads to more visits to the
physician, it is also possible that physicians respond to
people with a negative orientation in a way different
from those with a positive orientation.
Although we were unable to find any studies on this
issue amongst occupational samples, we did find one
simulation study (Ellington and Wiebe, 1999) in which
students were asked to role-play the symptoms of two
diseases – a minor throat infection and acute
appendicitis. Ellington and Wiebe’s results indicate that
84
those with a high negative orientation (specifically
neuroticism) are more likely to:
1) present information on their symptoms in a more
elaborate, detailed and intense manner to
physicians;
2) present information on recent stressors they had
experienced for the more serious disease to
physicians;
3) display visible signs of anger during the
consultation.
Ellington and Wiebe’s results also indicate that, during
consultation, physicians perceive that people with a
high negative orientation:
4) overact in the expression of their symptoms;
5) have an ulterior motive for the expression of their
symptoms;
6) are less likely to have a severe illness when
expressing the symptoms of a severe illness, such as
acute appendicitis;
7) have psychological causes as the primary
explanation of their illness, rather than physical
factors.
Ellington and Wiebe’s results also indicate that, even
when people present with physical symptoms,
physicians are more likely to:
8) schedule follow-up appointments with people with a
high negative orientation;
9) refer people with a high negative orientation to
mental health professionals;
10) prescribe psychiatric medication to people with a
high negative orientation.
Findings 7), 8), 9) and 10) indicate that monitoring
systems that rely on physicians’ reports could
overestimate the prevalence of psychological problems
amongst people with a high negative orientation, and
consequently diagnose conditions as being caused by
work-related stress, rather than as physical diseases with
no occupational cause. This conclusion should be taken
with extreme caution, however. Ellington and Wiebe
85
have presented only one study, which was a simulation
conducted with students with relatively inexperienced
physicians (all aged under 32). It is clear that more
research using occupational samples and more
experienced physicians is needed before any firm
conclusions can be drawn.
5.4.3. Negative orientation and health – is there
a really a link?
A negative orientation might also be related to physical
disease, as well as cause heightened experience of
symptoms. Friedman and Booth-Kewley (1987) have
outlined three ways in which this might be so:
1) Unhealthy habits: in that people with a negative
orientation might be prone, for example, to eat
unhealthy foods, smoke and drink more and exercise
less.
2) Biological third variables: in which a negative
orientation is associated with an underlying
physiological susceptibility to disease. This would
make it look like a negative orientation is causing a
disease, although the real cause is an undetected
susceptibility (see also Kirmayer et al, 1994);
3) Negative orientation as a cause of emotions: in
which the physiological changes associated with
negative emotions cause disease.
Those working in the field of physical illness and work­
related stress prefer an amended version of the third
process. In this tripartite model (Schwartz et al, 1996),
psychosocial hazards and individual differences
combine to produce an emotional response (see our
earlier description of the CAPHEM model for how this
might occur), which in turn might influence the course
of a specific disease, if the person has a physiological
susceptibility to develop that disease (Schwartz et al,
1996). This susceptibility need not be genetic, but can
be transient (e.g. salt in diet) or a combination of both
(e.g. cholesterol levels).
There is a problem with this simple model. The
cognitive and physiological components of emotion are
linked, but only loosely so (Lang, 1984). Therefore, it is
possible to feel anxious without showing the
physiological indicators of anxiety, and it is possible to
show the physiological indicators of anxiety without
feeling anxious. Therefore, for the tripartite model to
86
work, the cognitive experience of negative emotions
must be translated into the physiological experience.
Schwartz et al also indicate that prolonged effortful and
active coping attempts might also influence disease.
Where coping resources, such as control or support are
in short supply, such coping might place additional
demands on the body, so also leading to development of
disease.
5.5.
Individual differences and socio-cultural factors
It is clear from our review of different theories that both
individual differences in how we process information
about the environment and events that occur in that
environment alter how we experience, report and
express psychological and physical symptoms. Indeed,
it is a combination of both individual and work
characteristics that influences emotions, and emotions’
subsequent influence on health and perceptions of
health.
However, it is also clear from our review that although
a negative orientation is relatively stable, it can change
if the work environment changes. Indeed, wider social
and economic shifts (such as rising divorce rates, crime
and job insecurity) indicate that as whole we might be
becoming a more anxious, and hence negatively
orientated, society (Twenge, 2000).
Our theoretical review suggests that it is beliefs about
psychosocial hazards that, conjointly with the
occurrence of psychosocial hazards, influence
emotional reactions, and that changes in these beliefs
might gradually change a negative orientation to a more
positive orientation, or vice versa. Evidence suggests
that beliefs about psychosocial hazards are influenced
by three factors (Daniels et al, 2002c; Ettner and
Grzywacz, 2001; Harris, 2002):
i) The person: Here, beliefs about psychosocial hazards
are influenced by patterns of thinking and behaviour
learnt throughout our development, as well as genetic
susceptibility to think in a certain way. Indeed some
researchers have indicated that around a third of the
variation in job satisfaction is attributable to
differences in genetic make-up (Arvey, Bouchard,
Segal and Abraham, 1989). Certainly, if beliefs about
hazards are influenced by patterns of thinking learnt
early on in life, they will be automatic and
87
consequently very difficult to change (Power and
Dalgleish, 1997).
ii) The environment: Here, the nature of psychosocial
hazards experienced influence how we interpret them.
For example, if we learn that some work events do
prevent attainment of goals, then it is natural – not
irrational – to infer that future events of the same
nature will influence attainment of goals. If the
environment changes so that goals can be achieved,
then it is likely that beliefs about those events will
change too.
The distinction between ‘hard’ and ‘soft’
environments is usefully applied to environmental
influences (Frese and Zapf, 1994). A hard
environment is one which many people would
interpret the same way. For example, most people
would recognise that redundancy announcements
threaten economic and career goals, and so would
cause some level of anxiety to many people. A soft
environment is one that is open to more individual
interpretation, where the relationship to individual
goals is more idiosyncratic – such as many social
situations.
Changing the nature of ‘hard’ environments is most
likely to result in changes in beliefs about the work
environment. However, this does not mean nothing
can be done for ‘soft’ environments.
Where beliefs are shared amongst workers about a soft
environment, then changing the environment too
might prove an effective intervention. Indeed there is
evidence that beliefs about psychosocial hazards do
come to be shared, as we discuss next.
iii) Socio-cultural factors: These relate to how we learn
beliefs and develop our personality orientations
through interaction with our wider environment. The
influence of the environment can operate on several
levels. The most immediate level is that of the work
group and organisation. Indeed, others in the same
work group might even be able to influence our own
beliefs on a daily basis (Harris, 2002). Wider levels
include those that influence how society and how
groups within society operate. Indeed, our secondary
analysis shows differences are evident in negative
orientation between demographic groups. Values,
transmitted through widespread child rearing
practices, the education system, the mass media, trades
88
unions and professional associations all influence the
way we think about work and consequently how we
respond to work (e.g. Nyambegera, Daniels and
Sparrow, 2001). Such factors might influence how
psychosocial hazards are appraised in a given society,
perhaps making members of some societies or sub­
groups of society more likely to show a negative
emotional response some hazards rather than others.
From this review of influences on beliefs about
psychosocial hazards, it is clear that there are two main
ways of changing beliefs, which are complementary:
a) Change the work environment, so that people
develop new beliefs about it;
b) Change the way information about psychosocial
hazards is transmitted – at the level of the work
group, the organisation and within wider groups in
society.
In the next chapter, we will discuss in greater detail the
practical and research implications of our meta-analysis
and this theoretical commentary.
89
6. Conclusion
The major conclusions from this research are:
1) Psychosocial hazards are related to subsequent
reports of health (r=0.18).
a. This relationship is robust, and is evident even after
taking into account the influence prior health status
and individual differences.
b. Psychosocial hazards are more closely related to
psychological health than physical health. Any
influence of psychosocial hazards on physical health
is likely to be mediated through changes in health
behaviours or changes in physiological reactivity,
where an individual’s cognitive and physiological
systems are tightly coupled and there is a
physiological vulnerability.
c. It is likely that individual differences and
psychosocial hazards operate jointly to determine
how individuals interpret hazards and how
individuals cope with hazards.
2) A negative orientation is related to subsequent
reports of health (r=0.22).
a. This relationship is robust, and is evident even after
taking into account the influence prior health status
and psychosocial hazards.
b. The relationship is stronger for psychological health
and the cognitive experience of physical symptoms,
rather than physical illness itself.
c. There appears to be no difference in the magnitude
of the relationship between a negative orientation
that is a trait, an attitude or a state individual
difference.
d. A negative orientation might inflate reporting of
symptoms in epidemiological self-report surveys
and monitoring systems reliant on physician reports.
e. A negative orientation is more prevalent within
women and younger people of working age. There
are also differences between different socio­
economic groups and occupational groups.
90
3) There is great stability in psychological health and
negative orientation (r=0.59).
a. However, negative orientation can change, perhaps
due to incremental changes resulting from changes
in the environment or changes in socio-cultural
portrayals of work-related stress. Change is likely
to be slow and subtle.
In the following sections, we discuss the implications
for monitoring of work-related stress, organisational
practice and subsequent research arising from the
findings of the meta-analysis, secondary analysis of
other data-bases and our review of relevant theoretical
explanations.
6.1.
Implications for monitoring
Any social measurement is likely to contain some
degree of error, and this also applies to systems
designed to monitor work-related stress and associated
ill-health. Nevertheless, our interpretation of the results
of the meta-analysis, secondary analysis and review of
theoretical processes indicates where monitoring
systems might be more or less accurate.
a) Monitoring for stress-related symptoms is likely to
be more sensitive to changes in the work
environment if psychological rather than physical
symptoms are assessed;
b) Monitoring systems reliant on self-reports might
overestimate extent of symptoms attributable to
psychosocial hazards, especially physical
symptoms.
c) Monitoring systems reliant on physician reports
might overestimate the extent of psychological
problems attributable to work-related stress.
d) The over-estimation predicted in points b) and c)
might be more prevalent in certain groups of the
population where a negative orientation is more
prevalent, such as women or younger people. There
might also be differences that are more subtle,
between different socio-economic and occupational
groups. However, any differences between
demographic groups are likely to be small.
Discussion of theoretical paths also indicates that
changes in symptoms following improvements in work
91
conditions might take some time to be large enough to
detect. Even though changes in work conditions could
change health and make a negative orientation more
positive, such changes could take at least five years to
become apparent (cf. Dormann and Zapf, 2001).
6.1.1. Are measures too insensitive?
The high stability observed in psychological health in
the meta-analysis could reflect a certain degree of
insensitivity in measures of health and well-being.
In most studies, people are asked to give a generalised
impression of how they have been feeling over the past
few weeks, month, year, etc. Asking for such
generalised impressions assumes that people are capable
of recalling all relevant incidents and forming an
accurate judgement on the basis of those incidents. Of
course, this might be asking too much, and responses
might equally reflect underlying and immutable
personality factors as easily as real changes in health
(cf. Barrett, 1997).
Such problems might become magnified as the period
of assessment is increased. The practical implication
here, then, is that measures of health might be more
accurate if they ask respondents to indicate their health
over a relatively recent and well-defined period (i.e.
rating over the past month is better than rating over the
past year, rating over the past week is better than rating
over the past month). However, there is little or no
research on the relative accuracy of retrospective
reports of well-being or health in working samples over
time frames greater than a few days or weeks.
Therefore, there are currently no readily available
metrics to help guide decisions on trade-offs between
the accuracy of recent ratings and the wider coverage of
time periods associated with retrospective reports over
longer time frames.21
21
Parkinson et al (1995) report a study of mood ratings of 30 employed adults over
two working weeks. Participants were asked to rate mood several times a day, and
averaged daily mood ratings were compared to participants’ retrospective
assessment of mood for that day. Average momentary ratings of mood were highly
correlated with retrospective ratings of daily mood (0.66 for positive mood, 0.68
for negative mood). Parkinson et al also report a tendency for participants to
overestimate their levels of positive mood at the end of a daily basis, but not their
levels of negative mood.
92
Even so, like psychosocial hazards (Rick et al, 2001), it
is possible the most sensitive measures will use
frequency based scales to record symptoms, rather than
asking respondents to indicate the extent of their
agreement with statements about their health.
6.1.2. How to assess negative orientation
The findings of the meta-analysis indicate that a
negative orientation might lead to an overestimation of
some stress-related symptoms. Given that a negative
orientation might change only very slowly, this might
make it appear that a high frequency of stress-related
illness persists, even after real improvements have been
made.
It is therefore important that monitoring systems include
some assessment of negative orientation so that they can
take into account the level of stress-related symptoms
that could be attributable to a negative orientation.
Given the potential influence of occasion factors, it is
important that negative orientation be measured at the
same time as symptoms and hazards.
There exist short measures of negative orientation, such
as the negative affectivity scale from the PANAS
(Watson, Clark and Tellegen, 1988), and neuroticism
scales embedded in personality questionnaires such as
the Eysenck Personality Questionnaire (Eysenck and
Eysenck, 1975). However, given the importance of
assessing negative orientation, then a more detailed
consideration of existing measures might be called for.22
6.2.
Implications for practice
6.2.1. Cognitive and perceptual factors and the
individual
This report has focused on individual differences and
cognitive processes. It might then be tempting to
conclude that the causes of stress-related ill-health
reside within the individual, rather than work
environment. The results of the meta-analysis show
that individual differences are associated with
subsequent changes in health, and that individual
22
Such a review would need not only to examine the psychometric properties of
validity and reliability of measures, but also, for example, the extent they assess
the whole range of negative orientation, rather than a specific facet, as well as the
costs and benefits of measures that are free to use versus those that are
commercially available.
93
differences are associated with how people report
physical symptoms, but not more objective measures of
physical ill-health. However, blaming the individual
might be both erroneous and might focus attention on
only a few suitable interventions (see also, Daniels et al,
2002c).
As we have argued, individual differences represent
indicators of how people think. However:
a) Our meta-analysis indicates that psychosocial
hazards predict subsequent health above the effects
of individual differences and prior health status;
b) A range of theories we have reviewed and the
evidence underpinning those theories suggest it is
how individuals interpret psychosocial hazards that
is important in linking hazards to health;
c) Our theoretical review indices that interpretations of
psychosocial hazards might be influenced by
individual experience – dating back as far as early
childhood, but the work environment and the wider
socio-cultural environment might also influence
interpretations.
Consequently, although individually focused interventions
might be appropriate for some individuals, where hazards
are widely experienced and widely interpreted in much the
same way, interventions focused on work groups and
organisations might be more appropriate.
The finding that hazards are related to subsequent health,
as well as the theoretical review, might indicate the
suitability of job design interventions to prevent the
occurrence of psychosocial hazards or to promote
effective coping through, for example, providing
discretion over work activities or social support (cf.
Karasek and Theorell, 1990).
However, because the theoretical review indicates that
how we categorise psychosocial hazards might influence
how they affect us, there are potential implications for
how psychosocial hazards are assessed to form the
diagnosis for intervention. Our theoretical review might
indicate that psychosocial hazards should be assessed
using the categories and language used to describe those
categories common to a given organisation, occupation or
work group. In this way, more contextually specific
assessments might provide a better basis for targetted
intervention (Rick et al, 2002). This might then provide a
94
psychological basis for approaches that use assessments
tailored to specific work contexts (e.g. Cox, Griffiths,
Barlow, Randall, Thomson and Rial-Gonzalez, 2000).
The theoretical review also indicates that there might be
benefits from work conditions that promote progress
toward personal goals at a rate greater than an individual
might expect (see also Frese and Zapf, 1994). Again,
discretion over work or support might be important to
promote this, as this might allow individuals to pursue
their own goals. Additionally, performance management
might be useful here. We might expect to see better
psychological health in organisations where performance
management systems allow line managers to i) support
individuals to align their own goals with organisational
goals, ii) set time-frames for achieving goals and iii)
identify the means of attaining those goals.
6.2.2. Cognitive and perceptual factors and
society
We have concluded that socio-cultural factors might
influence how we interpret psychosocial hazards. We
based this conclusion on the secondary analyses and the
theoretical review.
Our theoretical review indicates that if we are led to
believe that something is psychologically harmful, then
it may, indeed, become so. Communicating how the
socio-cultural environment shapes our beliefs about
psychosocial hazards might be an important area for
intervention. Using the principles of good risk
communication,23 it might be necessary to inform public
debate so that, for example:
a) The debate does not become polarised between
those that would blame the individual and those that
catastrophise the contemporary work environment;
b) The public become aware of sensible, considered
scientific knowledge on the links between work,
cognition and health – as well as the limits of that
scientific knowledge;
c) It is clear what people, organisations and employee
associations such as trades unions can do to promote
well-being at work.
23
See, for example, Daniels (1996) and Daniels et al (2002c) for an application to
psychosocial hazards.
95
By focusing communication on real, rather than
imagined, hazards and what can sensibly be done about
them, then it might be possible to engender a more
positive and proactive orientation to work-related stress.
However, the extent to which various institutions (such
as the media, trades unions) influence our
interpretations of hazards is largely unknown.
Therefore, consideration of these socio-cultural factors
and potential relationships to risk communication might
be important.
6.3.
Implications for research
6.3.1. Need for detailed, time intensive studies
The theoretical review indicates that explanations for
the relationships between psychosocial hazards, health
and diagnosis accord a prominent position to dynamic
emotional processes that are both cognitive and social.
Our theoretical review also suggests that events, rather
than stable job conditions, are more proximal cause of
emotions and health.
It is therefore important that research does not
concentrate solely on large-scale longitudinal cohort
studies or intervention studies,24 in attempts to establish
causal relationships between stable job conditions and
health. Although small in number relative to cross­
sectional studies, such studies met the stringent
inclusion criteria for our meta-analysis and, therefore,
dominated our meta-analytic review. Whilst useful for
establishing whether there might exist generalisable,
causal relationships between variables, large cohort
designs have difficulty capturing the short-term
dynamic, interdependent and complex processes that
our theoretical review indicates underpin the
relationships amongst work, more stable individual
differences, health and closely related variables. For
example, our meta-analysis indicated that very few
longitudinal studies that met our strict inclusion criteria
had examined state individual differences. Rather
longitudinal studies of the kind included in our meta­
analysis focus more frequently on more stable
individual differences such as attitudes and personality.
However, without detailed consideration of the dynamic
processes underlying predictive associations found in
24
This is not to say such large scale longitudinal cohort or intervention studies
should no longer be conducted, as they are clearly useful and vastly superior, all
other things being equal, to cross-sectional designs.
96
longitudinal and intervention studies, it will not be
possible to interpret fully associations found in large
scale cohort designs. What is needed, then, to
complement cohort studies, are focused studies that
concentrate on the dynamic processes linking, for
example:
a) stable job conditions, personal factors and social
factors to the occurrence of distressing events at
work;
b) events in the work place to cognitive interpretation
of those events;
c) cognition to changes in emotion;
d) changes in cognition and emotion to coping choices
and subsequent action.
e) changes in cognitive processes and emotion to
social dynamics;
f) cognitive processes to physiological processes;
g) social processes of emotional expression to the
cognitive processes underlying physician diagnosis.
Best placed to research such questions, perhaps, are
well-designed studies concentrating on daily or
momentary changes or specific episodes over a period
of time. Whilst necessarily intensive in the time
required from participants, experimental methods, diary
methods and other event sampling methods might all be
useful in respect of these examining these detailed
processes as they occur.
The suggestion to research the social and cognitive
processes underlying diagnosis is an area of importance.
We were only able to find one study that examined this
issue (Ellington and Wiebe, 1999). Whilst the
implications of the study are worthy of detailed
consideration, the study suffers from a number of
design-flaws that make its findings tentative rather than
definitive.
6.3.2. Researching cognitive factors in the
workplace
In our meta-analysis, we used broad individual differences
as an indicator of differences in cognitive processes. In our
theoretical review, we indicated that cognitive processes
might influence how psychosocial hazards are categorised;
97
how implications for goals are appraised; how hazards are
linked to situations that have elicited emotions in the past;
and how decisions about coping behaviour are made. By
using broad measures of individual differences previous
research may have missed some of the intricacies of the
interplay between cognition and psychosocial hazards.
Indeed, some studies that have examined interactions
between individual differences and hazards have produced
conflicting results, even when using the same individual
differences and hazards (e.g. Daniels and Guppy, 1994;
Parkes, 1991).
Methods that can make cognitive processes more explicit
might be able to unravel more accurately the intricacies of
the relationships between cognition, psychosocial hazards
and emotions. One set of methods, called cognitive
mapping methods, can be used to show how people
understand the relations between hazards and emotions,
the causes of hazards, how to cope with hazards and the
likely consequences of emotions (Daniels et al, 2002c).
One practical advantage of using such methods is that the
methods can also help people articulate how they would
change the work environment, which can then be used to
feed into organisational change interventions (Harris,
Daniels and Briner, 2002).
Cognitive processes are, of course, dynamic: what we
think about changes from one moment to the next.
Therefore, methods will need to be developed that can
assess interpretations of hazards in a way to suit the
special circumstances of diary and event-sampling
studies. Only in this way will it be possible to capture
the relationships between hazards, cognitive processes
and emotions are they unfold in real contexts and in real
time.
There also remains a question concerning the nature of
psychosocial hazards. The meta-analysis found no
difference between objective and subjective measures of
hazards in the relationship between hazards and health.
This might reflect the small number of studies that have
used objective methods. Notwithstanding the issue of
events and their relationship to stable job conditions
highlighted above, it might be that it is very difficult to
obtain objective measures of some, if not most,
psychosocial hazards.
Many hazards are linked to the social fabric of
organisations, and therefore have no reality outside of
this social fabric. Unlike physical hazards, such as
noise, many psychosocial hazards have no material
98
reality. This has implications for measuring
psychosocial hazards, and a careful re-think might be
needed of the relationship between subjective and
objective measures. Different methods might be
tapping qualitatively different aspects of the nature of
hazards.
Given that many hazards inhabit a world defined by
social rather than physical interactions, the possibility
arises that different socio-cultural contexts might
promote the incidence of some, rather than other,
psychosocial hazards, as well the interpretation of those
hazards.
6.4.
Concluding remarks
In this report, we have shown that psychosocial hazards
and individual differences both influence how we
experience work-related health. It is clear, however,
these simple relationships do not convey the underlying
complexities of the interaction between a dynamic work
environment, dynamic cognitive processes and the
wider socio-cultural environment that influences the
experience, reporting and expression of work-related
stress. The complexity of these processes has
implications for how work-related stress is managed
and how that management is monitored.
99
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8. Appendices
A) Form used to sort the literature
107
Sort criteria
ID NO:
Decision (complete at end)
Include
[
]
Need full paper
[
]
Need to discuss
[
]
Potential for theory review
[
]
Reject
[
]
Where it is unclear from the abstract whether a paper meets the inclusion
criteria, then the full paper should be consulted.
Q1. Was the paper published in a peer reviewed journal? (please circle)
Yes
No
Unsure – need full paper
If no – discard as failing sort question 1
Q2. Evidence for which research question (please circle)
1a) Direct influence of individual difference
1b) Moderator influence of individual difference
Both
If research question – discard as failing sort question 2.
Unsure – need full paper
108
Q3. Type of individual differences examined (please tick all that apply)
a) Trait +ve orientation (e.g. agreeableness)
b) Trait –ve orientation (e.g. neuroticism)
c) Attitudinal/medium term stability +ve orientation (e.g. selfesteem)
d) Attitudinal/medium term stability –ve orientation (e.g. external
locus of control)
e) State +ve orientation (e.g. positive affect – enthusiasm,
activity)
f) State –ve orientation (e.g. negative affect – anxiety, anger
depression)
If none of the above – discard as failing sort question 3
If unspecified in abstract, obtain full paper
Q4. Type of study (tick all that apply)
Review paper
Meta-analysis
Theory paper
Empirical paper
Refer to KD to include elsewhere in report
Refer to KD to include elsewhere in report
Refer to KD to include elsewhere in report
Continue applying sort criteria
109
Q5. Method (please tick all that apply)
Classified as:
a) Experimental
Lagged
longitudinal
b) Quasi-experimental
Lagged
longitudinal
c) Panel study – health outcomes regressed on lagged
individual differences and stressors
Lagged
longitudinal
d) Diary study – health outcomes regressed on lagged
individual differences and stressors
Lagged
longitudinal
e) Panel study – health outcomes regressed on
contemporaneous individual differences and/or stressors
Contemporaneous
longitudinal
f) Diary study – health outcomes regressed on
contemporaneous individual differences and/or stressors
Contemporaneous
longitudinal
g) Cross-sectional
Crosssectional
Only articles falling into categories a, b, c and d included for review
110
Q6. Type of health state measured (please tick all that apply)
Health state
Self-report - acute psychological – e.g. mood
Self-report – acute physical - e.g. non-specific symptoms such as
headaches
Self-report – chronic psychological - e.g. Beck depression inventory
Self-report – chronic physical - e.g. self-reports of angina
Self-report – health behaviours - e.g. self-reported drinking
Physician report - acute psychological - e.g. acute anxiety disorder
Physician report – acute physical - e.g. influenza
Physician report – chronic psychological - e.g. diagnosed depression
Physician report – chronic physical - e.g. hypertension
Physician report – health behaviours - e.g. # visits to physician
Objective measures/research team diagnosis - acute psychological e.g. diagnosis by research team psychiatrist(s)
Objective measures/research team diagnosis – acute physical - e.g.
swabs taken for presence of flu bugs
Objective measures/research team diagnosis – chronic psychological e.g. diagnosis by research team psychiatrist(s)
Objective measures/research team diagnosis - chronic physical - e.g.
coronary heart diseased diagnosed by angiogram
Objective measures/research team diagnosis - health behaviours - e.g.
sickness absence from company records
If none of the above – discard as failing sort question 6
If unspecified in abstract, obtain full paper
Q7. Sample consists of working adults? (please circle)
Yes
No
If unspecified in abstract, obtain full paper
If no – discard as failing sort question 6
111
Q8. Type of psychosocial hazards examined (please tick all that apply)
1) Work demands - including:
1a) workload - quantity, pacing, time pressure, emotional content
of work;
1b) work scheduling – total hours, breaks, travel time, shift work;
1c) work organisation and job design – task design, team
working;
1d) physical environment – real and perceived danger, noise,
toxins;
2) Worker control or autonomy – including:
2a) Skill discretion – variety, opportunity for skill use;
2b) Decision authority – control and autonomy;
2c) Participation in decision making.
3) Support and interpersonal relationships in the working environment –
including:
3a) Proactive support – practical & emotional support, support
from work and
outside of work, feedback;
3b) Reactive support - practical & emotional support, support
from work and
outside of work, feedback;
3c) Team/intepersonal conflict, bullying and harassment;
Others - please specify
If no psychosocial hazards measured – discard as failing sort question 8
112
Final comments or discussion points
113
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