Running head: DATA DRIVEN POLICY MAKING
The Data Driven Policy Making Process
Lisa C. Bayhi
Southeastern Louisiana University
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DATA DRIVEN POLICY MAKING
The Data Driven Policy Making Process
Policy affects every aspect of our day to day functioning. Policy is so entrenched in our
lives it “directs how we complete tasks, guides how we travel, work, and play and determines the
limits of many of our freedoms” (Robinson, 2012). In this report an exploration of the health
policy that affects us all will be explored. Too global
Research Question
In order to develop the question for research addressing health policy, the PICOT clinical
research process was evaluated, but found to be lacking a policy focus. According to Hall &
Roussel (2012) health policy development models must include a process known as evidence
informed decision-making. Policy researchers must consider evidence in terms of “effectiveness,
appropriateness and implementability” (Robinson, 2012, p. 218). Several health policy
development frameworks and models were explored. The Data-Driven Policy Making model
first used by a government funded group known as the Agency for Healthcare Research and
Quality (AHRQ) was selected (Sonier, 2008). This model outlines a four-staged process
consisting of definitions and priorities, data, assessment and action questions (Robinson, 2012).
The first stage simply asks, what are the policy problems? Incorporation into this research
project asks the question, what are the policy barriers related to preventing nurse practitioners
(NPs) from autonomous practice or practicing to the full extent of their education and
preparation in the state of Louisiana? The second stage investigates available data to develop
policy; for this research, what data is available to support policy decisions regarding NPs
practicing to the full extent of their education and preparation in Louisiana will be answered.
The third stage of the model formulates the question, what does the data indicate about the
current state of affairs in Louisiana? And the fourth stage, identified as action, asks what policy
DATA DRIVEN POLICY MAKING
3
options for the state of Louisiana are supported by the data.?
Using the Data-Driven Policy Making process with an emphasis on evidence to develop a
course of action, culminates into the research question: Why do barriers continue to prevent
nurse practitioners from practicing to the full extent of their education and preparation in the
state of Louisiana, when the healthcare needs of its’ people are facing their greatest demands?
This is a good rhetorical question but not definitive enough for our purposes.
The Integrative Approach
An integrative approach was implemented for the literature search to answer the above
research question. The types of evidence elicited consist of systematic reviews, qualitative and
quantitative research as well as editorials and commentaries. Databases searched included:
CINHAL, Cochrane Library, Proquest Nursing and Allied Health. Time frames encompassed
the past five years, from 2008 to 2013. Keywords included health policy, public policy, nurse
practitioner practice, prescriptive authority, barriers to nurse practitioner practice, evidencebased health policy, advocacy, and healthcare reform. Exclusion criteria were defined as
geographic subsets outside of the United States. Inclusion criteria comprised all journal subsets,
English language, and all publication types. The research question was made broader to
accommodate more than Louisiana health care issues in order to generate results. Several
articles were deselected if they addressed the prescribing of a specific medication, addressed a
specific disease process, or a specific regulatory issue; for example, whistle blowers. Articles
were also deselected if they did not pertain to define the term then place abbreviation in
parenthese afterwards. Then you can use the abbreviation subsequently in the paper NP practice.
The Cochrane database word search criteria required the inclusion of the term nurse practitioners
but yields were a total of eight. The CINAHL database yield was 97 and the Proquest Nursing
DATA DRIVEN POLICY MAKING
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and Allied Health search produced 3769 articles, requiring the application of the inclusion and
exclusion criteria to sift through the data.
Summary of Findings
The literature review elicited a number of articles for inclusion, but also required further
analysis and synthesis to obtain a comprehensive database. For that reason, the research result
matrix summary of findings was separated into two tables. Table 1 exhibits the literature search
through CINHAL, Cochrane Library, ProQuest Nursing and Allied Health. The literature search
matrix is subdivided into authors, publication date, dependent variables, independent variables,
study design, findings and major subject headings. Editorials, untimely literature (older than five
years), and literature pertaining to healthcare systems outside of the United States were excluded
from the literature result matrix. Inclusion criteria for the literature matrix were articles
containing pertinent information on health policy, public policy, NP practice, prescriptive
authority, barriers to NP practice, evidence-based health policy, advocacy and healthcare reform.
Grey literature in the form of a doctor of philosophy thesis not usually referred to as a thesis if
doctorate work it should be a dissertation was retained since it discussed advanced practice
registered nurse (APRN) scope of practice. Many articles were found from state nursing
organizations providing information on current legislative issues furnished in state newsletters or
journals. Since many of these contained no bibliography and were considered a commentary on
state events or editorials, they were excluded from the matrix. All other obvious editorials or
commentaries were also eliminated. As the literature review progressed, obvious additional key
outcome research data was discovered in references of the literature review articles. Using the
ancestry literature approach, these additional relevant research studies were included in the
literature result matrix (Polit & Beck, 2012).
DATA DRIVEN POLICY MAKING
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The policy associated with NP practice in the state of Louisiana is derived from statutory
law as well as promulgated rules and regulations. Since this information is not in published
journals, an additional reference matrix was developed to contain this type of material. The
second matrix, illustrated in Table 2, comprises these policy reference documents including
statutes, rules and regulations, applicable policy reports and other governmental institution
information. Dependent variables, independent variable, publishing date and major finding
categories were included in the matrix. The reference material contained in matrix two clarifies
the present available information on APRN practice and policy.
Evidence Selection and Rankings
Selecting evidence to determine research rigor can be ranked into seven levels of strength
based on the effectiveness of an intervention as noted in Polit & Beck (2012). Evidence
hierarchies are configured to “rank types of research evidence sources according to the strength
of the evidence they provide” (Polit & Beck, 2012, p. 27). The most rigorous study, systematic
reviews of nonrandomized or randomized control trials are considered the pinnacle research,
indicated by level one. Other studies follow based on the evidence hierarchies to the lowest level
of seven; opinions of authorities and expert committees. The evidence hierarchy findings are
listed in numerical levels and supplied in Table 3. They display a summary of findings based on
the literature resources in Table 1, the literature review result and Table 2, the policy reference
matrix. The impact factor is a measure of citation frequency for an average article in a journal
(Polit & Beck, 2012). The impact factor of the peer reviewed nursing research journals are also
listed in Table 3 under the impact factor column.
For the health policy research literature review, systematic randomized control trial
literature was not applicable and consequently, no level-one or level-two evidence was available.
DATA DRIVEN POLICY MAKING
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The literature search did not reveal articles in the third level category. A few studies required
further evaluation to determine positions. An exemplar is the systematic review of Naylor &
Kurtzman (2010), it mentioned literature from a few randomized trials, but the researchers ____?
The table needs a title
DATA DRIVEN POLICY MAKING
TABLE 1
Authors
Watson, E
Hillman, H
(Watson &
Hillman, 2010)
7
Year
Published
Dependent
Variables
Independent Study
Variables
Design
Findings
Major
Subject
Journal of
Legal Nurse
Consulting
Autonomous
practice
Liability
Expanding practice roles will
likely increase the APRNs level
of accountability and liability
resulting in dual legal
responsibilities.
Advanced
nursing
Scope of
practice
Journal
Article
Descriptive
2010
Practice trends
Scope of
nursing
practice
McCracken, A
(McCracken,
2010)
Journal of
Gerontological
Nursing
Autonomous
practice
2010
Sampson, D
(Sampson,
2009)
Nursing
History
Review
2009
Autonomous
practice
Scope of
practice
Journal
Article
Healthcare
policy
Descriptive
Statutes
Case study
Regulation
Scope of
practice
Gerontological nurses are an
essential link in the delivery of
health care to older Americans.
And are essential stakeholders
in shaping healthcare policy. It
is time to share their expertise
at the advocacy table in
communities, in states, and in
nation.
Gerontologic
Nursing
Negotiating prescriptive
authority for APRNs.
Legislation
Health Policy
Prescriptive
Authority
History
New
Hampshire
8
DATA DRIVEN POLICY MAKING
Bodenheimer
T,
Pham, H
Health Affairs
Scope of
practice
2010
Access to
care
(Bodenheimer
& Pham, 2010)
Madler, B
Kalanek, C
Rising, C
Journal of
Nursing
Regulation
(Madler,
Kalanek, &
Rising, 2012)
2012
Williams, K
Kukla, M Bond,
G McKasson, M
Salyers MP
American
Journal of
Psychiatric
Rehabilitation
(Williams,
Kukla, Bond,
McKasson, &
Salyers, 2009)
2009
Autonomous
practice
Regulation
Statutes
Autonomous
practice
Scope of
practice
Journal article
Primary care crisis strategies for Access to
resolution.
primary care
Descriptive
historical
Journal
Article
Descriptive
Journal
Article
Case study
Research
survey design
Professional associations and
boards of nursing can work
together to promote the highquality patient care provided by
APRNs and ensure the
implementation of the APRN
Consensus Model.
Implementing
the APRN
Consensus
Model
While this study did not
demonstrate a decisive
advantage for either team in
providing quality services,
neither did it reveal any
evidence against substituting a
nurse practitioner for a
psychiatrist as medication
prescriber on assertive
community treatment teams.
We therefore recommend
further exploration of the nurse
practitioner option.
APRN
practice
outcomes
North Dakota
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DATA DRIVEN POLICY MAKING
Bahadori A
Fitzpatrick J
(Bahadori &
Fitzpatrick,
2009)
Journal of the
American
Academy of
Nurse
Practitioners
Autonomous
practice
Scope of
practice
Journal article
Descriptive
study survey
2009
Esperat M
HansonTurton,T
Richardson, M
Debisette, A
Rupinta, C
Journal of the
American
Academy of
Nurse
Practitioners
Autonomous
practice
Scope of
practice
Access to
care
Journal article
Descriptive
literature
review
2012
(Esperat,
Hanson-Turton,
Richardson,
Tyree, &
Rupinta, 2012)
Bauer J
(Bauer, 2010)
Journal of the
American
Academy of
Nurse
Practitioners
Autonomous
practice
Scope of
practice
Cost
effectiveness
Journal article
Economic
Analysis and
literature
review
2009
Correlational
Study provided evidence that
Primary Care NPs are highly
autonomous professionals and
continue to struggle with
empowerment. NPs
educationally prepared with a
better knowledge of legal and
political issues will be better
suited to influence healthcare
reform.
Nurse
Practitioners
Policy changes are essential to
assure that Nurse Managed
Health Clinics are an integral
part of the primary healthcare
safety net for America's
vulnerable populations, and that
advance practice nurses are at
the forefront of policy
initiatives.
APRN
NurseManaged
Centers
This paper presents extensive,
consistent evidence that nurse
practitioners provide care of
equal or better quality at lower
cost than comparable services
provided by other qualified
health professionals.
APRN
practice
Primary
Health Care
Professional
autonomy
Medically
underserved
Cost Benefit
Analysis
Health Care
Reform
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DATA DRIVEN POLICY MAKING
Wallerstedt D
Sangare J
Bartlett L
Mahoney S
(Wallerstedt,
Sangare,
Bartlett, &
Mahoney, 2009)
Villegas, W
Allen, P
(Villegas &
Allen, 2012)
Journal of the
American
Academy of
Nurse
Practitioners
Autonomous
practice
Journal article
Descriptive
research
survey
This survey shows the
variability in practice
opportunities available to NPs
in a research environment and
the impact they have on public
health.
The Journal of
Continuing
Education in
Nursing
Nursing
Economics
Autonomous
practice
Scope of
practice
Statutes
Journal article
Historical
descriptive
study
Regulation
Autonomous
practice
Access to
care
Journal article
Descriptive
analysis
2012
Scope-of-practice policy change
will occur through the
emergence of strong individuals
within nursing professional
organizations and the joining
together of organizations to
form one voice.
APRN workforce represents a
substantial source of human
capital to increase access to
cost-effective primary care.
Scope of
practice
Policy
Primary care
NPs
Physicians
Workforce
Hospitals
Health Affairs
2010
(Naylor &
Kurtzman,
2010)
U.S.
National
Institutes of
Health
Nursing Role
(Poghosyan,
Lucero, Rauch,
& Berkowitz,
2012)
Naylor, M
Kurtzman, E
APRN
2009
2012
Poghosyan, L
Lucero, R
Rauch, L
Berkowitz, B
Scope of
practice
Autonomous
practice
Statutes
Literature
review
Regulation
Scope of
practice
Correlational/
Descriptive
NPs contribute to high-value
primary care and full
integration is necessary for
patient access.
Barriers to
practice
NP scope of
practice
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DATA DRIVEN POLICY MAKING
Meyers, E
Doctoral
dissertation
Autonomous
practice
Scope of
practice
Dissertation
Recommendations on delivery
design of preventive healthcare.
Scope of
practice
(Meyers, 2009)
Agosta, L
(Agosta, 2009a)
2009
Access to
care
Journal of
Nursing
Measurement
Scope of
practice
Access to
care
2009
Journal article
Correlational
survey
Patient
satisfaction
Pohl, J
Hanson, C
Newland, J
Cronenwett, L
Health Affairs
Autonomous
practice
Access to
care
2010
Scope of
practice
(Stempniak,
2013)
Hospitals &
Health
Networks
2013
Access to
care
Journal article
Descriptive
analysis
Journal article
Descriptive
Scope of
practice
The Nurse Practitioner
Satisfaction Survey was found
to be reliable and valid for
measuring patient satisfaction
with nurse practitioner
delivered primary health care
services.
Patient
satisfaction
NP
Occupational
health
Instrument
(Pohl, Hanson,
Newland, &
Cronenwett,
2010)
Stempniak, M
NP practice
This article recommends
substantive changes in the way
health care professionals in all
disciplines are trained, and in
their roles, so that patients can
receive appropriate and costeffective care from skilled and
fully functional health care
teams.
Primary care
Physician Assistant and NP use
to assist in primary care.
NP
Access to care
NP scope of
practice
Physician
assistant
Primary care
Access to care
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DATA DRIVEN POLICY MAKING
Agosta, L
(Agosta, 2009b)
Journal of the
American
Academy of
Nurse
Practitioners
Scope of
practice
2009
Patient
satisfaction
Access to
care
Journal article
Correlational
survey
Overall the population seeking
health care was satisfied with
NP services. In particular,
married or cohabitating subjects
reported general satisfaction
scores that were statistically
significantly higher than those
who were single and never
married. No other differences
were found.
NP
Patient
satisfaction
Employee
health
Occupational
health
Instrument
development
Poghosyan, L
Nannini, A
Smaldone, A
Clarke, S
O'Rourke, N
Rosato, B
Berkowitz, B
(Poghosyan et
al., 2013)
Policy, Politics
& Nursing
Practice
Autonomous
practice
Scope of
practice
Regulation
2013
Statute
Journal article
Qualitative
analysis
This study utilized qualitative
descriptive design to investigate
NP roles and responsibilities as
primary care providers in
Massachusetts and their
perceptions about barriers and
facilitators to their scope of
practice issues.
Primary care
Scope of
practice
NP barriers
Massachusetts
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DATA DRIVEN POLICY MAKING
Newhouse, R
Stanik-Hutt, J
White, K
Bass, E
Wilson, R
Weiner, J
Nursing
Economics
Autonomous
practice
2011
Access to
care
Scope of
practice
Journal article
Systematic
review of
outcome data
This systematic review supports
a high level of evidence that
APRNs provide safe, effective,
quality care to a number of
specific populations in a variety
of settings.
Health care
policy
Studies
Nurses
Hospitals
Physicians
(Newhouse et
al., 2011)
TABLE 1: The Literature Review Matrix
NOTE. APRN = Advanced Practice Registered Nurse; Autonomous practice = nurse practitioners practicing to the full extent
of their education and preparation, NP = nurse practitioner
TABLE 2
Pub Yr
Dependent
Variables
Independent
Variables
Major Findings
Institution
(Louisiana State
Board of Nursing,
2012)
2012
Regulation
Rules and regulations for
APRN practice in the state of
Louisiana.
(Institute of
Medicine, 2010)
2011
Scope of
practice
The Future of Nursing:
Leading Change, Advancing
Health, The Robert Wood
Foundation.
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DATA DRIVEN POLICY MAKING
(Louisiana Nurse
Practice Act, 2010)
2010
Statutes
Legal reference for APRN
practice in the state of
Louisiana.
(Koh & Sebelius,
2010)
2010
Scope of
practice
Promoting prevention through
the Affordable Care Act as
presented in the New England
Journal of Medicine.
Regulation
APRN Consensus
Work Group &
the National
Council of State
Boards of Nursing
APRN Advisory
Committee, (2008)
2008
Regulation
APRN Advisory Committee
on the Consensus Model for
APRN Regulation: Licensure,
Accreditation, Certification &
Education.
(The American
Association of
Colleges of
Nursing, 2006)
2006
Advocacy
The Essentials of Doctoral
Education for Advanced
Practice Nursing.
TABLE 2: The Policy Reference Matrix
NOTE. APRN = Advanced Practice Registered Nurse
DATA DRIVEN POLICY MAKING
did not supply statistical comparison data between the research studies, and was accordingly
placed in level-five; systematic review of descriptive, qualitative and physiologic studies. The
literature review levels of all evidence are illustrated in Table 3.
The literature with the overall highest level of evidence was found in level four; single
correlational and observational studies. Two studies, Agosta (2009a) and Williams, Kukla,
Bond, McKasson, & Salyers (2009) both examined client satisfaction with healthcare delivery
by providers. These two studies delivered statistical analysis of the survey tools used and both
revealed the highest level of evidence among the total articles in the literature review. The
Agosta (2009a) study obtained patient surveys from those individuals who were provided care by
a nurse practitioner. The Williams et al. (2009) study compared client satisfaction surveys
between a nurse practitioner led assertive community treatment (ACT) team and a psychiatrist
led ACT team. In addition, this study compared team cohesiveness through a survey of the
members of each ACT team. Since the Williams et al. (2009) research actually compared the
two different healthcare provider outcomes; it was deemed a more rigorous study. An internet
search to establish the impact factor for the Williams et al. (2009) article published in the
American Journal of Psychiatric Rehabilitation did not yield sufficient data to evaluate the
journal and assign an impact factor. Nonetheless, it was deemed the highest level of evidence for
this literature review.
Evidence Critique
Quantitative Critique: "Can a Nurse Practitioner Serve in the Prescriber Role on an Assertive
Community Treatment Team?”
This is the evidence critique for the research study providing the highest level of evidence
from the literature review. In this qualitative research article, surveys, interviews and
DATA DRIVEN POLICY MAKING
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TABLE 3
Level of Evidence
Author
Number of
Articles
N=24
Publication
Impact Factor
Journal of American Academy of
Nurse Practitioners
0.91
(Williams et al., 2009)
American Journal of Psychiatric
Rehabilitation
Unknown
(Wallerstedt, Sangare, Bartlett,
& Mahoney, 2009)
Journal of American Academy of
Nurse Practitioners
0.91
(Agosta, 2009a)
Journal of Nursing Measurement
0
(Naylor & Kurtzman, 2010)
Health Affairs
0
Level 1
a. Systematic RCTs
b. Systematic
Nonrandomized Trials
Level 2
a. Single RCT
b. Single
Nonrandomized Trials
Level 3
Systematic Review of
Correlational/
Observational Studies
0
0
0
Level 4
(Agosta, 2009b)
Single Correlational/
Observational Study
5
(21%)
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DATA DRIVEN POLICY MAKING
Level 5
Systematic Review of
Descriptive/Qualitative/
Physiologic Studies
(Esperat et al., 2012)
3
Journal of American Academy of
Nurse Practitioners
0.91
(Bauer, 2010)
(12%)
Journal of American Academy of
Nurse Practitioners
0.91
Dissertation
0
Journal of American Academy of
Nurse Practitioners
0.91
(Sampson, 2009)
Nursing History Review
0
(Watson & Hillman, 2010)
Journal of Legal Nurse Consulting
0
(Poghosyan et al., 2013)
Policy, Politics & Nursing Practice
0
(Pohl et al., 2010)
Health Affairs
0
(Bodenheimer & Pham, 2010)
Health Affairs
0
(Poghosyan et al., 2012)
Nursing Economics
0
(Villegas & Allen, 2012)
Journal of Continuing Education in
Nursing
0
(Meyers, 2009)
Level 6
Single
Descriptive/Qualitative/
Physiologic Study
(Bahadori & Fitzpatrick, 2009)
10
(25%)
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DATA DRIVEN POLICY MAKING
Level 7
Opinions of Authorities,
Expert Committees
(Madler et al., 2012)
Journal of Nursing Regulation
0
(McCracken, 2010)
Journal of Gerontologic Nursing
0.82
(Louisiana Nurse Practice Act,
2010)
6
Louisiana Statute
(42%)
(Louisiana Rules and
Regulations, 2012)
Louisiana Nursing Rules
& Regulations
Institute of Medicine
Robert Woods Foundation Report
Consensus Model Licensure,
accreditation, certification &
education
National Council of State Boards of
Nursing
Essentials of Doctoral
Education for Advanced
Nursing Practice
American Association of Colleges
of Nursing
(Stempniak, 2013)
Hospitals & Health Networks
0
TABLE 3: Levels of Evidence
NOTE: RCTs = Randomized Control Trials
DATA DRIVEN POLICY MAKING
assessments were employed to compare psychiatrist prescriber led ACT teams and nurse
practitioner prescriber led ACT teams. The results revealed no decisive advantage of either
group over the other and the researchers encouraged further exploration of the nurse practitioner
option (Williams et al., 2009).
Sampling
The study population was identified and described as consumers at the Adult & Child
Center in Indianapolis, ID served by a psychiatrist led ACT team and those served by a nurse
practitioner led ACT team (Williams et al., 2009). Eligibility criteria were specified as all
practitioners at least half-time and consumers served by the two ACT teams. Consumers who
had a legal guardian were excluded from the sampling (Williams et al., 2009).
All consumers meeting the exclusion criteria from each prescriber led ACT team were
solicited by the authors to complete the survey. This was on a volunteer basis, described as a
“natural experiment” (Williams et al., 2009, p. 209). Notation data lists 200 total consumers
solicited with 95 participants; 55 consumers served by the psychiatrist led ACT team and 40
consumers served by the nurse practitioner led ACT team completed the survey. The sampling
method was expected to afford a representative sample of the community served, but not the
general population. According to Polit and Beck (2012), selecting study participants from
multiple sites could have increased the study’s generalizability. This would have enhanced the
diversity represented in the sample population. Another option would have been to expand the
time period of the survey resulting in an increased sample size; however, this may not have been
realistically achievable. Future studies using this process could enhance the outcome data. All
10 nurse practitioners and 13 psychiatrists participated in an interview type survey. All team
members of each prescriber led ACT team were “assessed regarding their adherence to the ACT
DATA DRIVEN POLICY MAKING
20
model and to antipsychotic medication management practices, using standardized fidelity scales”
(Williams et al., 2009, p. 209).
The method used for participants recruited to the sample included providing information
regarding the research project and their voluntary participation. The study states “case managers
distributed the survey packets to those consenting to the study” (Williams et al., 2009, p. 214).
No data is supplied by the researchers as to why clients chose not to participate. Additionally,
since this clinic was the only location surveyed, the method does in fact suggest selection bias.
According to Fawcit & Garity (2009), a sample of a controlled group such as clinic patients
lends concern regarding the authors’ motivation, as they are a “captured population”. Were they
willing volunteers or did they volunteer out of obligation, gratitude, or fear? This creates
volunteer bias.
The key characteristics of the sample were described in the inclusion criteria obtained in
the article comprising demographic data of age, gender, race, ethnicity, marital status, and years
of education. In addition, practitioners information on number of months in the field of mental
health services and number of months employed at their current position on the team were
obtained (Williams et al., 2009). The researchers explored several areas of vulnerable sampling
biases including, according to Williams et al. (2009), consideration of severity of illness,
diagnosis, and living status of the team’s caseload.
The sample size for this research was sufficiently large to support statistical conclusion
validity for certain aspects of the study. The researchers included the use of Cohen’s d to
measure and justify effect size on the consumer satisfaction with medication management
survey. Williams et al. (2009) states a moderate effect size for this survey was calculated with d
ranging from .56 to .67. According to Polit and Beck (2012) most nursing studies cannot
21
DATA DRIVEN POLICY MAKING
anticipate effect sizes larger than .50 and an effect size within this range is considered a medium
effect (Polit & Beck, 2012). This study exceeded expectations and by doing so, decreased the
risk of Type II errors for this survey. Nevertheless, a larger, more diverse sample size obtained
from other geographical locations would have delivered a better extrapolation to the general
population.
Data Collection
There is congruence between the researched variables as conceptualized and as
operationalized in the introduction and methods section of this study. The dependent variable is
identified as the satisfaction with care provided by either the psychiatrist led or nurse practitioner
led ACT team. Methods for measures were subdivided into categories, each with a designated
tool for evaluation and included a sufficient reliability assessment. Williams et al. (2009) noted
consumer satisfaction was measured with a “modified version of the Patient Perception of
Medication Services Scale (PPMS)” (p. 211). This 15-item checklist used a modified ordinal
three-point Likert scale with an “internal consistency of good (Cronbach’s alpha = .82)”
(Williams et al., 2009, p. 211).
The team cohesion element was developed by the authors using
a 20-item survey with a five-point Likert scale and Cronbach’s alpha =.87 (Williams et al.,
2009). According to Polit and Beck (2012), Cronbach’s alpha is a widely used reliability index
to measure internal validity of a composite measure with the normal range of values between .00
and +1.00, with the large values reflecting higher internal consistency. Therefore, both the above
measurement tools presented sufficient internal validity and did reliably measure key attributes
of the data collection instrument based on Cronbach alpha scores. This evidence nonetheless, is
based on the current research sample itself since these particular measurement tools were either
modified or created for this particular research study. It is reasonable to conclude that data
DATA DRIVEN POLICY MAKING
22
quality would be similar to the research sample, but further testing of these instruments is
necessary.
In the Teague et al study (as cited in Williams et al., 2009) fidelity to the ACT model was
evaluated by the Dartmouth ACT scale (DACTS) to discriminate well-implemented ACT
programs from other types of case management services. This is a 28-item scale rating
completed during a daylong site visit by a fidelity authorized assessor using a five-point scale
indicating degrees of program implementation (Williams et al., 2009). The research report offers
evidence of the reliability of these measures through previous research by McHugo et al. (as
cited in Williams et al., 2009) demonstrating the tool has “adequate internal consistency,
acceptable-to-excellent interrater reliability, and sensitivity to change over time” (p. 213). It’d
be nice to knoe the results. Furthermore, this fidelity testing tool is implemented to acquire
accreditation in the Indiana state system. The fidelity to medication management practice
guidelines were measured using the two part Organizational and Prescriber Medication
Management (MedMAP) fidelity five-point scale assessment also completed through a fidelity
authorized assessor site visit. The two sections measure the prescriber and the organization
fidelity management of medications. This is a nationally implemented survey and the authors
state empirical norms have not yet been established (Williams et al., 2009). These two
measurement tools offer evidence of the reliability of the measures and it is reasonable to
conclude that data quality would be similar to this research sample.
Finally, a prescriber interview was conducted using 20 open-ended questions and seemed
to elicit subjective, qualitative information only (Williams et al., 2009). The authors did not
stipulate reliability evidence for this tool. This qualitative examination could have included
more information on the analysis method employed. A discussion was included, but no patterns
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DATA DRIVEN POLICY MAKING
of behavior, themes or fracturing strategies were identified as suggested in Polit and Beck
(2012). A demographic survey was also developed and utilized by Williams et al. (2009) to
assist with the identification of background variables of study participants that could influence
outcome data. The authors offer limited information regarding the tool utilized to capture
demographic information, although age, gender, race, ethnicity, marital status, and years of
education data were obtained. The researchers did not report minimum reliability measures for
the demographic tool.
Validity information was not reported in this study and no content validity index (CVI)
was furnished. Inferences could be made that the two fidelity testing instruments were valid
based on their use in the Indiana state accrediting process and on a national level. According to
Polit and Beck (2012), content validity is relevant for both affective and cognitive measures. The
modified PPMS is a tool developed for the measurement of the affective domains related to
consumer satisfaction by the Texas Medication Algorithm Project completed by Miller
et al. (as cited in Williams et al., 2009). The team cohesion measurement tool was developed by
the authors themselves and had no prior validity testing or expert review. Polit and Beck (2012)
state there is no completely objective method to ensure content coverage of an instrument,
however it is common to use a panel of experts to evaluate the content validity of new
instruments. Although Cronbach testing was competed for these measurement tools, no panel of
experts was employed for an official content review. Thus, validity is questionable and requires
further testing and evaluation for verification.Good report on reliaibility & validity
Statistics
Descriptive Statistics
Descriptive statistics were included in the research report and overall sufficiently
DATA DRIVEN POLICY MAKING
24
described the major characteristics of the data set. According to Williams, et al. (2009) both the
consumer and practitioner group characteristic descriptive statistical analysis was conducted.
Percentages were used in the demographic analysis as well as the mean with standard deviation
applied to the Likert scale measurement tools appropriately. The authors further described the
practitioner comparison characteristics as revealing no significant statistical differences
“according to years of education, age, sex, or race” (Williams et al., 2009, p. 215). The
percentages and means were included for all demographics, but deficits were found in the data
given including the omission of actual median and standard deviation values in some instances.
Median was not reported with the mean of participant age or education level. The study reported
no skewed data that would require the use of the median in lieu of the mean. This appears to be
the correct descriptive statistic for the authors’ purpose; however, as noted by Polit and Beck
(2012), median data may have been more appropriate for years of education, as the mean may
give a distorted effect by averaging extremes. A better illustration of consumer survey
participation, response rate and attrition could have been made with the use of descriptive
percentages. A statement of total clients seen and the number of participants was employed
instead. Descriptive statistics were not used to answer research questions when inferential
statistics would have been more appropriate. For each of the satisfaction survey questions the
authors reported mean information for both study groups. Overall, the authors fairly examined
and included the methodological features, key variables, attrition and response rates in detail
with further discussion noted in the data analysis section.
Inferential Statistics
Multiple bivariate inferential statistics were reported with the application of Pearson’s
chi-square and t test to measure and correlate the strength of the relationship between
DATA DRIVEN POLICY MAKING
25
independent variables. Results were defined and enumerated within the study outcomes based
on examination of the two teams using “t tests and chi squares as appropriate… t tests were also
used to examine differences between the two teams on the attitudinal scales, using two-tailed
tests with a .05 significance level” (Williams et al., 2009, p. 214). Most likely the demographic
data was nominal and the satisfaction data ordinal, but this is only assumed. You can usually
figure the demographics are a mixture of nominal and ordinal data.
The researchers utilized Pearson’s chi-square test, which is a better fit for a larger sample
population, but is suitable for the data level exemplified (Polit & Beck, 2012). In regards to
fidelity testing scales of the ACT implementation medication management practice guidelines
however, “no statistical test was possible” and results were compared to normed data by William
et al. (2009). It is reasonable to conclude since these tools are used in the state of Indiana and the
nation for ACT testing and have expert panel reviews, they are valid. All other hypothesis and
research questions had an appropriate application of statistical testing as described above. Given
the level of measurement of the variables and the nature of the hypothesis the selected tests were
appropriate.
Three hypotheses were explored in this research study. The first was consumers on a
team with a nurse practitioner for medication management would report satisfaction equivalent
to the psychiatrist medication management led team. The second hypothesis was the members of
a team with a nurse practitioner would report higher levels of team cohesion than members of a
team with a psychiatrist. The first and second hypothesis was not supported by the outcome
data, but an explanation for the findings was analyzed. The third hypothesis was each team
would receive equivalent ratings on a medication management fidelity scale; results revealed
partial support (Williams et al., 2009).
DATA DRIVEN POLICY MAKING
26
Tables were included which comprised descriptive and inferential statistical analysis of
each item in the measurement tools of consumer satisfaction with medication management, ACT
model implementation and team cohesiveness. The mean, standard deviation, t test and p value
were included along with team comparisons. The researchers also detailed how the consumer
satisfaction tool designed in alternating shaded patterns for questions presented an unexpected
difficulty. Williams et al. (2009) in response to this issue states “of 95 consumers completing the
survey, 26 had two or more items inadvertently left blank” and “mean substitution methods were
used to account for missing data as appropriate” (p. 212). The use of both inferential and
descriptive statistics was on the average, appropriate and effective.
Parametric testing.
The authors did not utilize parametric testing, only the use of non-parametric testing
including Pearson’s chi square and t tests were employed. The selected statistical tests were
appropriate for the given level of measurement of the research variables and the nature of the
hypothesis. Demographic data was considered nominal and the satisfaction data ordinal, but the
authors did not specifically state this information. Williams et al. (2009) do not offer a rationale
for using these statistical tests. According to Polit and Beck (2012), if the variable measurement
is indeed nominal and/or ordinal, parametric? I think you mean non-parametric testing is
appropriate for both the demographic analysis as well as the satisfaction analysis. It is not known
whether more powerful parametric tests could have been utilized.
Statistical significance.
The p level to determine statistical significance was p >.05 I think you mean <.05
(Williams et al., 2009). No statistical tests employed by researchers proved to be statistically
significant. Although the effect size was reported to be medium, calculated using Cohen’s d,
DATA DRIVEN POLICY MAKING
27
resulting in .56 to .67 for the consumer satisfaction with medication administration management
tool, no other actual effect size data was furnished. Williams et al. (2009) reported no
statistically significant differences overall between the two ACT groups or the population served.
None of the statistical tests were noted to be statistically non-significant. Subsequently, further
analyses of comparison data with additional studies with larger effect sizes are necessary to
determine clinical significance and applicability. For exploratory purposes, the authors
“inspected differences at the item level for the attitudinal surveys” (Williams et al., 2009, p.
214). Nonetheless, the two fidelity testing tool averages (without statistical application)
imparted possible differences. The ACT implementation tool revealed a higher score for the
psychiatrist led ACT team, stated to be 4.35 in comparison to the nurse practitioner led ACT
team of 3.65. The authors did note this data may be skewed due to the pursuit of the ACT
accreditation process by the psychiatrist prescriber led team. The fidelity to medication
management revealed the opposite, with a higher overall score of 3.05 for the nurse practitioner
led ACT team, versus a score of 2.82 for the psychiatrist led ACT team. Further discussion
revealed the national average sample score for this measurement tool is 3.0, consequently both
prescribers were determined to be in the acceptable range (Williams et al., 2009). There was no
definitive explanation offered as to the higher nurse practitioner prescriber led ACT team
score.You probably need to break up this paragraph
According to Polit and Beck (2012), a Type II error can occur when a false negative
conclusion is accepted. Large sample sizes are required to control for Type II errors. In this
study because of the smaller sample size, a Type II error could have occurred. Williams et al.
however, do not address this possibility, although they do recommend repetitive research for
validation. The lack of study outcome data identifying a true relationship between variables also
DATA DRIVEN POLICY MAKING
28
demonstrates a statistical conclusion validity threat. Since there was no statistical difference
found between the two ACT teams, and given the size of the sample, external validity of the
study is also jeopardized.
Multi-variate statistics.
The researchers for this study utilized no multi-variate statistics and it is doubtful if their
addition would have added to the study. The demographics between the two consumer groups
were nearly identical, with a mean of 12 years education for both groups and a mean age of 53
for the psychiatrist group and 44.5 for the nurse practitioner group. In addition, the “two teams
did not significantly differ according to years of education, age, sex, or race” (Williams et al.,
2009). The researchers could have investigated linkages among three or more variables and
tested whether hypothesized pathways from cause to effect are consistent with data (Polit &
Beck, 2012). Moreover, causal modeling could have been a possible method to incorporate
using a path analysis to evaluate causal links among variables. Sample size although taken into
account may have affected the validity and future research is necessary for comparison
data.Good
Data Analysis, Interpretation, and Recommendations
Williams, et al. (2009) included a lengthy discussion on limitations of the study and
possible effects on the credibility of the research evidence. All limitations and validity threats
however, were not noted. For example, the authors did not acknowledge a double Hawthorne
effect since clients and staffs were aware of their participation in the study. No explanation was
postulated as to why clients chose not to complete the survey for the study. Patients who were
truly unsatisfied with their treatment plan may not have participated in the study due to the
voluntary nature of the survey creating volunteer bias. An additional factor threatening the
DATA DRIVEN POLICY MAKING
29
study’s validity unmentioned by the researchers, was the number of new clinic admission
patients cared for during the evaluation period for each ACT team. Interpretation of the higher
nurse practitioner led ACT fidelity to medication management scoring was poorly explained.
Threats and potential biases mentioned by the researchers included several issues that
could have affected the study’s statistical conclusion validity. The larger psychiatrist led team
fidelity score for ACT implementation may have been due to the known pursuit of ACT state
accreditation, creating bias. Caseload differences may also have created a threat to validity, for
example, severity of illness, diagnosis and living status as noted by the authors. In addition,
Williams et al. (2009) states another confounding variable was the nurse practitioner dual role of
prescriber and team leader. For future studies, the recommendation was made to separate these
two roles to better compare the two ACT teams. Further limitations noted by the study included
“newly developed and unvalidated scales and low participation rates on the consumer surveys”
(Williams et al., 2009, p. 221). The consumer sample size was small with sufficient power to
detect only a large effect. As no significance was noted between the two teams and the null
hypothesis was retained, the overall evidence was persuasive. No mixed significance was noted.
No correlation was made regarding the results of this study and other research studies. The
authors applied two bivariate testing statistics to verify no significant difference in the
satisfaction scores between the two sample ACT teams. And appropriately, this hypothesis was
not sustained.
Persuasive evidence was offered in support of the study interpretation including findings
from other studies. The conclusion section refers to a meta-analysis suggesting that “while
fidelity to the organizational features of an ACT team are predictive of client outcomes, fidelity
to the staffing requirements of the ACT model is not associated with reduced hospitalizations”
DATA DRIVEN POLICY MAKING
30
(Williams et al., 2009, p. 222). Furthermore, the researchers suggest development of standards
for staff roles on ACT teams based on needed competencies. No unwarranted causal inferences
were made and alternative explanations for the findings were considered plausible. The rationale
for questioning the higher psychiatrist fidelity score was credible in light of the determination of
the ACT team to achieve state ACT accreditation. The study notes this state accreditation was
successfully obtained several months after the completion of the research project (Williams et
al., 2009). Because there is a plausible alternative for the outcome of the data received, the
conditions necessary for causality are jeopardized.
In conclusion, the researchers did discuss possible implications regarding significance to
clinical practice, despite the non-significant findings, stating if the “feasibility of using a nursepractitioner in lieu of a psychiatrist is evaluated on the basis of general satisfaction without
regard to comparisons with the conventional team, then it could be argued that, by virtue of
overall high satisfaction ratings, this arrangement is feasible” (Williams et al., 2009, p. 221).
There was no discussion of nursing theory, however, future research was recommended. Clinical
significance could not be extrapolated to the general population and further testing of the patient
satisfaction instrument was encouraged in other healthcare settings as well as a larger sample
sizes. Weighing the stated implications and given the study’s limitations and the magnitude of
the effects as well as evidence from other studies, one could reasonably justify the use of nurse
practitioner prescriber led ACT teams for future clinical practice.
Conclusions and Recommendations
In conclusion, the literature review imparted needed data to fulfill the research question.
Based on three major healthcare policy movements, The Institute of Medicine report on the
future of nursing, the Affordable Care Act and the Consensus Model for APRN regulation,
DATA DRIVEN POLICY MAKING
31
Louisiana requires healthcare changes to meet the needs of the future. The Institute of Medicine
(IOM) report (2010) recommended four key messages to shape the future of nursing. 1. Nurses
should practice to the full extent of their education and training. 2. Nurses should achieve higher
levels of education and training through an improved education system that promotes seamless
academic progression. 3. Nurses should be full partners, with physicians and other health care
professionals, in redesigning health care in the United States. 4. Effective workforce planning
and policy making require better data collection and information infrastructure (Institute of
Medicine, 2010).
The goal of the Affordable Care Act is to increase access for all patients to affordable
health insurance. According to Koh & Sebelius (2010) this expanding coverage will be
established by a health insurance marketplace in every state and by the increase in access to the
Medicaid program. Overall, more patients are to have healthcare access.
The consensus model for APRN practice is an effort to create a more uniform licensure,
accreditation, certification & education process for every state in the nation (APRN Consensus
Work Group & The National Council of State Boards of Nursing APRN Advisory Committee,
2008). The Louisiana State Board of Nursing has begun the process towards application of this
model for APRNs in the state of Louisiana and further policy changes are required for full
implementation.
These three central documents outline the format for the future of NP practice and are the
focus to answer the research question, why do barriers continue to prevent nurse practitioners
from practicing to the full extent of their education and preparation in the state of Louisiana,
when the healthcare needs of its’ people are facing their greatest demands? With the knowledge
gained from the policy statements, outcome research, and healthcare trend data specified in the
DATA DRIVEN POLICY MAKING
32
literature review, an evidence supported health policy action plan can be developed. The two
studies from the literature review signifying the highest level of evidence, reveals overall patient
satisfaction with NP care. Moreover, additional studies from the review afford evidence of the
quality of healthcare provided by nurse practitioners. This solidifies the ability of NPs to provide
primary healthcare needs for the state of Louisiana. The recommended time frame for the
project is 18 months in order to include the 2014 Louisiana Legislative Session that begins
March 10, 2014 and ends June 2, 2014.
The American Association of Colleges of Nursing (AACN) has advocated heath policy as
an essential element to doctoral nursing education (AACN, 2006). Doctoral prepared nurse
practitioners (DNPs) are charged with the role of leadership in health policy. In order to meet
this guideline, the creation of a DNP coalition group to formulate guidelines, tactics, ideas, and
recommendations on health policy issues in conjunction with the Louisiana Association of Nurse
Practitioners will be fashioned.
Health policy efforts must be coupled with professional relationships to achieve greater
health for our state and for our future. Through evidence based health policy action plans, nurse
practitioners can overcome barriers and practice to the full extent of their education and
preparation in order to meet the healthcare needs of the people of Louisiana.How will you use
this information? What is the next step?
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References
Advanced Practice Registered Nurses (APRN) Consensus Work Group & The National Council
of State Boards of Nursing APRN Advisory Committee. (2008). Consensus model for
APRN regulation: Licensure, accreditation, certification & education. Washington, DC.
Retrieved from Retrieved from http://www.nursingworld.org/Documentvault/
Agosta, L. J. (2009a). Psychometric Evaluation of the Nurse Practitioner Satisfaction Survey
NPSS). Journal of Nursing Measurement, 17(2), 114–133. doi:10.1891/1061-3749.17.2.114
Agosta, L. J. (2009b). Patient satisfaction with nurse practitioner-delivered primary healthcare
services. Journal of the American Academy of Nurse Practitioners, 21(11), 610–7.
doi:10.1111/j.1745-7599.2009.00449.x
Bahadori, A., & Fitzpatrick, J. J. (2009). Level of autonomy of primary care nurse practitioners.
Journal of the American Academy of Nurse Practitioners, 21(9), 513–9. doi:10.1111/j.17457599.2009.00437.x
Bauer, J. C. (2010). Nurse practitioners as an underutilized resource for health reform: Evidencebased demonstrations of cost-effectiveness. Journal of the American Academy of Nurse
Practitioners, 22(4), 228–31. doi:10.1111/j.1745-7599.2010.00498.x
Bodenheimer, T., & Pham, H. H. (2010). Primary care: Current problems and proposed
solutions. Health Affairs, 29(5), 799–805. doi:10.1377/hlthaff.2010.0026
Esperat, M. C., Hanson-Turton, T., Richardson, M., Tyree, D. A., & Rupinta, C. (2012). Nursemanaged health centers: Safety-net care through advanced nursing practice. Journal of
the American Academy of Nurse Practitioners, 24(1), 24–31. doi:10.1111/j.1745-
DATA DRIVEN POLICY MAKING
34
7599.2011.00677.x
Fawcit, J., & Garity, J. (2009). Evaluating research for evidence-based nursing. Philadelphia,
PA: F. A. Davis.
Institute of Medicine. (2010). The future of nursing : Leading change, advancing health.
Washington, DC: The National Academies of Press.
Koh, H. K., & Sebelius, K. G. (2010). Promoting prevention through the Affordable Care Act.
The New England Journal of Medicine, 363(14), 1296–9. doi:10.1056/NEJMp1008560
Louisiana Nurse Practice Act, Pub. L. No. § 37:911 et seq. (2010). Retrieved from
http://www.lsbn.state.la.us/Portals/1/Documents/rules/npafull.pdf
Louisiana State Board of Nursing. (2012). Rules and Regulations. Title 46. Professional and
Occupational Standards Part XLVII Nurses. Retrieved from
http://www.lsbn.state.la.us/Portals/1/Documents/rules/fullrules.pdf
Madler, B., Kalanek, C. B., & Rising, C. (2012). An incremental regulatory approach to
implementing the APRN consensus model. Journal of Nursing Regulation, 3(2), 11–16.
McCracken, A. L. (2010). Advocacy: It is time to be the change. Journal of Gerontological
Nursing, 36(3), 15–7. doi:10.3928/00989134-20100202-05
Meyers, E. (2009). Capturing the advanced practice nursing voice for implementation of
preventive services. (Doctoral dissertation, Azusa Pacific University). Retrieved from
http://ezproxy.selu.edu/login?url=http://search.proquest.com/docview/305160133?accountid
=13772
Naylor, M. D., & Kurtzman, E. T. (2010). The role of nurse practitioners in reinventing primary
care. Health Affairs, 29(5), 893–9. doi:10.1377/hlthaff.2010.0440
DATA DRIVEN POLICY MAKING
35
Newhouse, R. P., Stanik-Hutt, J., White, K. M., Bass, E. B., Wilson, R. F., & Weiner, J. P.
(2011). Advanced Practice Nurse Outcomes 1990-2008: A Systematic Review. Nursing
Economics, 29(5), 230–250.
Poghosyan, L., Lucero, R., Rauch, L., & Berkowitz, B. (2012). Nurse practitioner workforce: A
substanitial supply of primary care providers. Nursing Economics, 30(5), 268–294.
Poghosyan, L., Nannini, A., Smaldone, A., Clarke, S., O’Rourke, N. C., Rosato, B. G., &
Berkowitz, B. (2013). Revisiting scope of practice facilitators and barriers for primary care
nurse ractitioners: A qualitative investigation. Policy, Politics & Nursing Practice.
doi:10.1177/1527154413480889
Pohl, J. M., Hanson, C., Newland, J. A., & Cronenwett, L. (2010). Unleashing nurse
practitioners’ potential to deliver primary care and lead teams. Health Affairs, 29(5), 900–5.
doi:10.1377/hlthaff.2010.0374
Polit, D. F., & Beck, C. T. (2012). Nursing research: Generating and assessing evidence for
nursing practice (9th ed.). Philadelphia, PA: Wolters Kluwer Health| Lippincott Williams &
Williams.
Robinson, C. B. (2012). Chapter 12. In H. Hall & L. Roussel (Eds.), Evidence based practice: An
integrative approach to research, administration and practice (pp. 215–231). Burlington,
MA: Jones & Bartlett.
Sampson, D. A. (2009). Alliances of cooperation: Negotiating New Hampshire nurse
practitioners’ prescribing practice. Nursing History Review, 17(2009), 153–78. Retrieved
from http://www.ncbi.nlm.nih.gov/pubmed/20067085
Sonier, J. (2008). Data-driven policy decisions : Uses of Minnesota hospital data. United States
Department of Health & Human Resources. Agency for Healthcare Research and Quality.
DATA DRIVEN POLICY MAKING
36
Retrieved from http://archive.ahrq.gov/qual/kt/workshop1208/sonier3.htm
Stempniak, M. (2013). Closing the primary care gap. Hospitals & Health Networks, 87(3), 45–
52, 1. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/23617120
The American Association of Colleges of Nursing. (2006). The essentials of doctoral education
for advanced nursing practice (pp. 1–28). Retrieved from
http://www.aacn.nche.edu/publications/position/DNPEssentials.pdf
Villegas, W. J., & Allen, P. E. (2012). Barriers to advanced practice registered nurse scope of
practice: Issue analysis. Journal of Continuing Education in Nursing, 43(9), 403–9.
doi:10.3928/00220124-20120716-30
Wallerstedt, D. B., Sangare, J., Bartlett, L. D., & Mahoney, S. F. (2009). The unique role of
advanced practice nurses at the National Institutes of Health: Results of a 2006 survey.
Journal of the American Academy of Nurse Practitioners, 21(7), 351–7. doi:10.1111/j.17457599.2009.00419.x
Watson, E., & Hillman, H. (2010). Advanced practice registered nursing: Licensure, education,
scope of practice, and liability issues. Journal of Legal Nurse Consulting, 21(3), 25–30.
Williams, K., Kukla, M., Bond, G. R., McKasson, M., & Salyers, M. P. (2009). Can a nurse
practitioner serve in the prescriber role on an assertive community treatment team?
American Journal of Psychiatric Rehabilitation, 12(3), 205–224.
doi:10.1080/15487760903066339