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This assignment asks you to examine and summarize a performance improvement model (as discussed in week 5 and listed below), using a real from a Canadian healthcare organization’s quality improvement...

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This assignment asks you to examine and summarize a performance improvement model (as discussed in week 5 and listed below), using a real from a Canadian healthcare organization’s quality improvement initiative.

  • PDSA
  • Rapid Cycle Improvement
  • FOCUS- PDCA
  • FADE
  • LEAN
  • SIX SIGMA
  • LEAN SIX SIGMA

Many examples of improvement projects conducted by healthcare organizations can be found in academic literature or on the internet.

  • Find 1 example of aCanadian healthcare quality improvement projectand summarize the improvement models used, including diagrams and charts to demonstrate the models discussed.
  • Summarize the main principle(s) of the improvement model used and explain how the concepts and techniques worked in practice.
  • Discuss the background of the healthcare issue identified, the current state and improvement outcome goals in the real healthcare quality improvement project you found.
  • Performance improvement projects consist of four steps, describe how the improvement project steps used in your example in detail.
    1. Define the improvement goal
    2. Analyze current practices
    3. Design and implement improvements
    4. Measure success
  • Provide your own conclusion on the success of the initiative and any recommendations you would have in terms of other quality improvement tools that could be used, improvements to methodology and areas for further quality review.
  • Ensure that you provide the case study in your submission (i.e. hyperlink or download and submit of the case study document).

Assignment submission is a max of 3 double-spaced pages, using 12 point font. APA citations must be included and a reference page is in addition to the 3 page limit.

*** As there are a high volume of examples available to use for this assignment, it is expected you find aunique case study to use. There should not be duplication of case studies used in the class.****

Answered 1 days After Feb 17, 2022

Solution

Bhawna answered on Feb 18 2022
106 Votes
doi:10.1016/j.evalprogplan.2007.01.002
ARTICLE IN PRESS
0149-7189/$ - se
doi:10.1016/j.ev
�Co
espond
E-mail addre
(M.L. Rusch),
jsands@sparc.
(J. Frankish),
Evaluation and Program Planning 30 (2007) 115–124
www.elsevier.com/locate/evalprogplan
Key considerations for logic model development in research
partnerships: A Canadian case study
Sarah J. Fielden, Melanie L. Rusch, Mambo Tabu Masinda,
Jim Sands, Jim Frankish, Brian Evoy�
Institute of Health Promotion Research, University of British Columbia, 2206 East Mall, LPC Room# 435 Vancouver, BC, Canada V6T 1Z3
Received 26 September 2006; received in revised form 5 December 2006; accepted 16 January 2007
Abstract
Community-academic partnership research is a fairly new genre of community-based participatory research. It has arisen in part, from
ecognition of the potential role of alliances in the development and translation of applied knowledge and the elimination of health
disparities. This paper reports on the learning process of academic and community members who worked together in developing a logic
model for a research program focusing on partnerships with vulnerable populations. The Partners in Community Health Research is a
6-year training program that seeks to combine research, training, and practice through the work of its ‘‘learning clusters’’. As these types
of partnerships proliferate, the articulation and exploration of clear models will assist in their implementation. The authors, coming from
oth academia and community agencies, present a logic model meant to facilitate program management. Key considerations in the
model’s development are discussed in the context of an ongoing research partnership; namely, the complexity of the research partnership,
power and accountability, alignment with health promotion policy, and the iterative nature of program design. Recommendations
challenge academics, policy-makers, service providers, and community members to reflect on the elements needed to support and manage
esearch partnerships and the tools necessary to ensure continued collaboration.
2007 Elsevier Ltd. All rights reserved.
Keywords: Logic model; Partnership; Program evaluation; Community-based research; Training program; Vulnerable populations
1. Introduction
Health research in Canada is experiencing a metamor-
phosis in response to the paradigm shift toward Population
Health, the creation of
oader training programs within
the Canadian Institutes of Health Research (CIHR), and
the voices of a sometimes disenfranchised public. Commu-
nity-based participatory research (CBPR) is meant to
foster research that is collaborative, participatory, empow-
ering, systematic, and transformative (Hills & Mullett,
2000), alternative to traditional methods of generating
knowledge. In the past decade, CBPR projects have
e front matter r 2007 Elsevier Ltd. All rights reserved.
alprogplan.2007.01.002
ing author. Tel.: +604 822 2258; fax: +604 822 9210.
sses: [email protected] (S.J. Fielden), [email protected]
[email protected] (M.T. Masinda),
c.ca (J. Sands), [email protected]
[email protected] (B. Evoy).
proliferated as an approach to research that seeks to
involve community members, organizational representa-
tives, and researchers in all aspects of the research process
while sharing expertise, responsibilities and ownership
(Israel, Schultz, Parker, & Becker, 2000). As such it
invokes in its actors intersubjective and self-reflexive
processes.
Community-academic research partnership is a fairly
new genre of CBPR. It has arisen from recognition of the
potential role of alliances in the development of and the
need for translation of applied knowledge and the
elimination of health disparities. Research collaborations
and the use of quality improvement processes guided by
Plan-Study-Do-Act cycles (Speroff & O’Connor, 2004) are
most common in the health sector and are believed to lead
to more effective clinical programs. Frankish and collea-
gues provide a summary of the underlying theoretical and
empirical reasons for collaboration between the health
www.elsevier.com/locate/evalprogplan
dx.doi.org/10.1016/j.evalprogplan.2007.01.002
mailto:[email protected]
mailto:[email protected]
mailto:[email protected]
mailto:
[email protected]
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S.J. Fielden et al. / Evaluation and Program Planning 30 (2007) 115–124116
sector and academe or the public (Frankish, Kwan, Larsen,
Ratner, Wharf-Higgins, 2002; Frankish, Larsen, Ratner,
Wharf-Higgins, & Kwan, 2002). It remains unclear as to
how, and if such partnerships can also lead to enhanced
community health and development (Cu
ie et al., 2005).
In parallel, there is an increased call for research that is
elevant to decision-making at all levels: from individual
health choices to national and international health systems
and policy. Partnerships between academic institutions and
organizations such as those that deliver healthcare and
social services represent one strategy that offers promise in
terms of
idging the gap between knowledge and practice.
As a result of this perceived benefit, agencies in North
America such as the Canadian Institute of Health Research
(2005) the Michael Smith Foundation for Health Research
(MSFHR) (2006) and the Centre for Disease Control
(1994) have earmarked funds for collaborative health
esearch initiatives with industry, community organiza-
tions, clients, and researchers.
In recent years, as research partnerships establish
themselves, researchers have begun to identify the strengths
and challenges of particular research partnerships (Bazzoli
et al., 2003; Elsinger & Senturia, 2001; Israel, Schulz,
Parker, & Becker, 1998). They have also begun to uncove
some of the elements useful to their evaluation (Cu
ie
et al., 2005; Winnipeg Inner City Alliance, 2005). However,
the structure of these collaborations is highly variable and,
due to their infancy and uniqueness, few tools exist to guide
the development, implementation, and evaluation of such
projects.
Logic models have been espoused as essential fo
delivering theoretically sound, evidence-based programs
(Moyer Verhovsek, & Wilson, 1997). As such, they may be
instrumental in conceptualizing, planning and implement-
Steering C
Community
Health Services
Research Cluste
Vulnerable
Population
Research Cluste
Workplace Health
Research Cluste
Manage
• Commu
• Financi
Manag
• Program
Manag
• Recruit
Applica
Fig. 1. PCHR
ing successful research-community partnerships. This
paper highlights a partnership program that focuses on
populations experiencing health disparities. It describes the
‘‘Vulnerable Populations Cluster’’—a ‘‘learning cluster’’ of
the Partners in Community Health Training Program.
During cluster development and evolution, the perceived
need and potential benefit of a more detailed framework
for action was identified. We present the resulting logic
model developed to facilitate future program management.
We then discuss key considerations that were identified by
cluster members during the development of the model, and
lessons learned by these members.
2. Program description
The Partners in Community Health Research (PCHR)
training program is a collaborative 6-year training initia-
tive based out of university and health region research
institutes in British Columbia, Canada and funded by the
CIHR and MSFHR. The program is based on the
following fundamental principles: co-learning, power shar-
ing in all stages of research design and sharing resources.
Its objectives are multi-sectoral and include research
training, capacity-building, fostering evidence-based deci-
sion-making, and the creation of new resources. The
PCHR organizational structure is illustrated in Fig. 1 and
consists of three ‘‘research clusters’’, each with a specific
focus. Clusters operate relatively independent, and foste
‘‘cluster-based learning’’ as a pedagogical tool. Table 1
outlines a glossary of terms used throughout the paper and
the objectives of the Vulnerable Populations (VP) Cluste
as well as those of the overall PCHR program are
epresented in Table 2.
ouncil
ment Team
nications
al
ement
ement
ment and
tion
Cross-Cluster Committees
& Working Groups


Learning
Innovation
Events and
Workshops
• Learne
Recruitment and
Selection
• Program
Evaluation
• Ad hoc
structure.
ARTICLE IN PRESS
Table 1
Glossary of terms
Glossary of terms
Primary collaborative partnership Working intensely with community groups on collaboratively identified research project(s)
Consultative services Offering consultative services to community groups at varying levels of research projects
Multi-organizational
idging Working in an over-arching manner to
ing together community groups with similar interests in vulnerable
populations; organizing forums, opening networking lines and creating learning opportunities
Vulnerable population Social groups who have an increased susceptibility to or higher than average risk of health-related problems
(Flaskerud & Winslow, 1998)
Health equity The absence of systematic disparities in health (or its social determinants) between more or less advantaged social
groups (Braveman & Gruskin, 2003)
Decision-less decisions Institutional inaction on an identified issue perceived to be politically sensitive (McCullum, Pelletier, Ba
,
Wilkins, & Habicht, 2004)
Power The capacity to produce intended, foreseen, and unforeseen effects on others based on ability to control access to
valued resources (McCullum et al., 2004)
Individual empowerment Increase in self-esteem or confidence which evolved from collective action (Laverack & Wallerstein, 2001). To
this definition, we add that it is possible for individuals to empower themselves without collective action. Fo
example, an individual may read a book or watch a television program that triggers a self-reflexive process that
leads them to change their own circumstance
Cluster-based learning Clusters allow for community-based learning environments; this approach means shared learning across diverse
university and community target groups through project-based teamwork on actual community research
priorities. Cluster-based learning opportunities will stem from each cluster’s work plan (www.pchr.net)
Reflexivity ‘‘[S]erious attention is paid to the way different kinds of linguistic, social, political and theoretical elements are
woven together in the process of knowledge development, during which empirical material is constructed,
interpreted and written’’ (Alvesson & Sköldberg, 2000, p. 5)
Workable agreement Through a process of deliberation, people agree upon a route of action but for diverging reasons (Dryzek, 2000)
Table 2
Goals and objectives of PCHR and the VP cluste
PCHR goals
� Providing research training in an integrated, transdisciplinary community partnership approach that links research to policy and practice
� Developing capacity of community practitioners and university researchers to engage in community partnership research that contributes to sustained
partnerships
� Educating researchers, policy makers, and community members to create evidence for best practices
� Developing and disseminating cu
iculum materials for use regionally, nationally, and internationally
Vulnerable populations cluster goals
� Building an effective, efficient, fun, and supportive research training team-addressing issues related to research with vulnerable populations
� Building capacity among the team members as community health researchers through training and learning opportunities
� Building research capacity amongst community health researchers by developing a program of research with at least one partner in relation to literacy,
food security, and youth-at-risk
� Building capacity by providing research and evaluation consultations in the areas of literacy, food security and youth-at-risk
� Using networking and learning sessions as a strategy to build capacity to work with vulnerable populations among academics, community
professionals and community agencies by
idging research, community, and policy
S.J. Fielden et al. / Evaluation and Program Planning 30 (2007) 115–124 117
In
ief, the VP cluster, as a sub-cluster of the PCHR
training program, consists of mentors, graduate students,
and ‘‘community learners’’. Specific funds are used to ‘buy’
the time and involvement of mid-career professionals (as
community learners) and graduate learners. Their partici-
pation is intended to develop the ‘receptor capacity’ of
partner organizations, i.e., their ability to receive and use
esearch tools and results.
The membership includes professors, representatives from
provincial health authorities and community-based organiza-
tions, community healthcare practitioners, post-doctoral
fellows, and university graduate students. The specific
institutions involved in the VP cluster membership include
the Institute of Health Promotion Research (IHPR) at the
University of British Columbia (UBC), and the Vancouve
Coastal Health Authority (VCHA). Members are from
various multi-disciplinary backgrounds and from multiple
sectors including academic and community mentors, and
academic and community learners. Partnering agencies
include Vancouver Coastal Health’s Sharon Martin Com-
munity Health Trust Fund (SMART Fund) and its affiliated
non-profit agencies, the Social Planning and Research
Council (SPARC BC) of British Columbia, and various
health and literacy focused non-governmental organizations.
http:
www.pchr.net
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S.J. Fielden et al. / Evaluation and Program Planning 30 (2007) 115–124118
The mission of the VP cluster involves developing
models and strategies to build capacity amongst academics,
community professionals and community members in
elation to vulnerable populations, by
idging research,
community and policy. The members share a common
interest in generating knowledge and fostering skills in
community-based research practice with those populations
experiencing the largest health disparities due to societal
marginalization. These interests are reflected in the goals
and objectives of PCHR and the VP cluster described in
Table 2. These were established in the early years of the
program and were used to inform the various components
of the logic model that was later developed (Fig. 2). Fo
example, the consultations refe
ed to in the VP cluste
goals were included in the work plan activities and could
feasibly contribute to increased community skill sets
(‘‘enabling output’’) and the ‘‘long-term impact’’ of
sustained research networks. The goals included in the
table focus on the training and research elements of the
program whereas the logic model elaborates on these by
PROGRAM
ACTIVITIES
Workplan
• Primary collaborative
partnership
• Consultative services
• Multi-organizational
Bridging
• Training
Partnerships
• University
• Agencies
• Communities
Materials/Resources
• Literature Reviews
Toolkits
• Resource compilation
• Training workshop
• Group discussions/ learning
opportunities / sharing of
experiences
IMMEDIAT
OUTPUT
Predispo
• Positive atti
toward partn
and research
• Increase in
knowledge f
learners & m
Enablin
• Increase in sk
• Ready resourc
Tool kits
• Cluster Struct
focus on goals
• Co-learning
community an
academic mem
Reinforc
• Sustained lin

• Career oppo
INPUTS
People
• Vulnerable
Populations (VP)
Cluste
• Vulnerable
Populations
• Agencies working
with VPs
• Other interested
parties
Resources
• Training Fellowships
for VP members
• Materials created by
VP cluste
• Cross-cluster training
Strategic Planning
• Mission, Goals
• Assess/Apply fo
funding
• Evaluation
Marketing
In all levels strive to work collaboratively with p
of the project and intended
Program rec
Institutions
• Research
Institutes
• Health Services
• University
Fig. 2. VP cluste
creating categories and elucidating the intended benefits on
the program to communities.
Empirical evidence has shown that health disparities
exist and sub-divide along lines of social determinants that
include ethnicity, gender, and socio-economic status
(Flaskerud et al., 2002; FTP Advisory Committee on
Population Health, 1999; Mu
ay, 2004; Ram, 2005;
Williams & Collins, 1995). Constructs of vulnerability are
linked to notions of ‘‘health equity’’ and social structures
that confer agency and power. ‘‘Vulnerability’’ can result
from financial circumstances, place of residence, health,
age, functional or developmental status, and personal
characteristics such as race, ethnicity, or sex (Purdy,...
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