Community Resource Paper
You are to identify five social support resources in your community that are geared toward and beneficial to older adults (>age 65).
The summary is to be a maximum of 8 pages not including the title and reference pages which are required. There is a minimum of 5 references. Be aware of the need to properly paraphrase or quote information from written documents of the facilities. Remember to include reference(s) for demographic information.
I. Describe community where you live (urban/rural, percentage of population by age and income, etc.)
II. Identify 5 resources geared toward older adults, one for each of the levels of care listed. In your own words (do not copy and paste from facility website or brochure) describe the services provided.
a. Independent Self-Care
b. Acute Care Discharge
c. Long-Term Care
d. Family Care Giving
e. End of Life Care
III. Discuss the following:
a. Are the resources accessible to all older adults in your community? Why or why not?
b. Could an aging adult seamlessly transition from each level of resource (continuum of care)?
c. What additional resources might be needed in your community to enhance the older adults' safety and quality of life?
Rubric
Comm Resources
Comm Resources
Criteria
Ratings
Pts
This criterion is linked to a Learning OutcomeDescribes community
10.0 pts
Proficient
Describes community thoroughly with supporting demographic data, including geriatric population
6.0 pts
Developing
Describes community using broad statements with general demographic data
1.0 pts
Beginning
Names city/state, describes few or no general demographic data
10.0 pts
This criterion is linked to a Learning OutcomeIdentifies, describes 5 resources
50.0 pts
Proficient
Identifies 5resources geared toward older adults and in own words describes services. The resources address Independent Self-Care, Acute Care Discharge, Long-term Care, Family Caregiving, and End-of-Life Care. Discussion includes sufficient and accurate detail on all 5 required questions to address.
30.0 pts
Developing
Identifies 5resources geared toward older adults and describes services with overuse of direct quotes from agency publications. The resources may not address all required stages of Independent Self-Care, Acute Care Discharge, Long-term Care, Family Caregiving, and End-of-Life Care. Discussion lacks sufficient and accurate detail on all 5 required questions to address.
1.0 pts
Beginning
Identifies The resources do not address all required stages of Independent Self-Care, Acute Care Discharge, Long-term Care, Family Caregiving, and End-of-Life Care. Discussion lacks sufficient and accurate detail on all 5 required questions to address.
50.0 pts
This criterion is linked to a Learning OutcomeSummary
15.0 pts
Proficient
Are these resources accessible to all older adults in your community? Why or why not? 2 points Could an aging adult seamlessly transition from each level of resource (continuum of care)? 10 points What ad.
1. Community Resource Paper
You are to identify five social support resources in your
community that are geared toward and beneficial to older adults
(>age 65).
The summary is to be a maximum of 8 pages not including the
title and reference pages which are required. There is a
minimum of 5 references. Be aware of the need to properly
paraphrase or quote information from written documents of the
facilities. Remember to include reference(s) for demographic
information.
I. Describe community where you live (urban/rural, percentage
of population by age and income, etc.)
II. Identify 5 resources geared toward older adults, one for each
of the levels of care listed. In your own words (do not copy and
paste from facility website or brochure) describe the services
provided.
a. Independent Self-Care
b. Acute Care Discharge
c. Long-Term Care
d. Family Care Giving
e. End of Life Care
III. Discuss the following:
a. Are the resources accessible to all older adults in your
community? Why or why not?
b. Could an aging adult seamlessly transition from each level
of resource (continuum of care)?
c. What additional resources might be needed in your
community to enhance the older adults' safety and quality of
life?
Rubric
Comm Resources
Comm Resources
Criteria
2. Ratings
Pts
This criterion is linked to a Learning OutcomeDescribes
community
10.0 pts
Proficient
Describes community thoroughly with supporting demographic
data, including geriatric population
6.0 pts
Developing
Describes community using broad statements with general
demographic data
1.0 pts
Beginning
Names city/state, describes few or no general demographic data
10.0 pts
This criterion is linked to a Learning OutcomeIdentifies,
describes 5 resources
50.0 pts
Proficient
Identifies 5resources geared toward older adults and in own
words describes services. The resources address Independent
Self-Care, Acute Care Discharge, Long-term Care, Family
Caregiving, and End-of-Life Care. Discussion includes
sufficient and accurate detail on all 5 required questions to
address.
30.0 pts
Developing
Identifies 5resources geared toward older adults and describes
services with overuse of direct quotes from agency publications.
The resources may not address all required stages of
Independent Self-Care, Acute Care Discharge, Long-term Care,
Family Caregiving, and End-of-Life Care. Discussion lacks
sufficient and accurate detail on all 5 required questions to
address.
3. 1.0 pts
Beginning
Identifies The resources do not address all required stages of
Independent Self-Care, Acute Care Discharge, Long-term Care,
Family Caregiving, and End-of-Life Care. Discussion lacks
sufficient and accurate detail on all 5 required questions to
address.
50.0 pts
This criterion is linked to a Learning OutcomeSummary
15.0 pts
Proficient
Are these resources accessible to all older adults in your
community? Why or why not? 2 points Could an aging adult
seamlessly transition from each level of resource (continuum of
care)? 10 points What additional resources might be needed in
your community to enhance the older adults safety and quality
of life?3 points
8.0 pts
Developing
Are these resources accessible to all older adults in your
community? Why or why not? 2 points Could an aging adult
seamlessly transition from each level of resource (continuum of
care)? 10 points What additional resources might be needed in
your community to enhance the older adults safety and quality
of life?3 points
1.0 pts
Beginning
Are these resources accessible to all older adults in your
community? Why or why not? 2 points Could an aging adult
seamlessly transition from each level of resource (continuum of
care)? 10 points What additional resources might be needed in
your community to enhance the older adults safety and quality
of life?3 points
15.0 pts
4. This criterion is linked to a Learning OutcomeAPA Style &
Format
15.0 pts
Proficient
Utilize APA format margins, fonts, spacing, headings, title
page(2 points) Maximum 8 pages narrative not including title
and reference pages (required) (2 points) Correct spelling,
grammar, narrative flow (2 points) Correct use of citations and
references (5 points) Minimum 2 scholarly non-text references
(within last 5 years) (4 points)
8.0 pts
Developing
Utilize APA format margins, fonts, spacing, headings, title
page(2 points) Maximum 8 pages narrative not including title
and reference pages (required) (2 points) Correct spelling,
grammar, narrative flow (2 points) Correct use of citations and
references (5 points) Minimum 2 scholarly non-text references
(within last 5 years) (4 points)
1.0 pts
Beginning
Utilize APA format margins, fonts, spacing, headings, title
page(2 points) Maximum 8 pages narrative not including title
and reference pages (required) (2 points) Correct spelling,
grammar, narrative flow (2 points) Correct use of citations and
references (5 points) Minimum 2 scholarly non-text references
(within last 5 years) (4 points)
15.0 pts
Total Points: 90.0
Is Access Sufficient?
An Examination of the Effects
of the MedShare Program to
5. Expand Access to Prescription
Drugs for Indigent Populations
Thomas Shaw
University of South Alabama, Mobile
Mark Carrozza
Institute for Policy Research, Cincinnati, Ohio
We conduct an evaluation of MedShare, a program designed to
enhance access
to prescription drugs for indigent patients in the Greater
Cincinnati area. The
program expands access to drugs by providing subsidies to
reduce the costs
paid by patients for their prescriptions. The assumption is that
by expanding
access to prescription drugs, participant health outcomes as
measured by qual-
ity of life improve. Although the program appears outwardly
successful, we
found little difference between program participants and
comparison groups.
We feel that these findings point to a major flaw with existing
health policy:
access alone is not sufficient to improve health outcomes. Too
often programs
are created and, provided they show outwards signs of success
(e.g., enrollment
and utilization), are assumed to be improving the health of the
community. Our
findings indicate that one must look beyond just expanding
access to ensure
that programs are indeed achieving their overall objectives.
Keywords: health; prescriptions; evaluation; policy; access
7. for services
is a critical component in reducing disparities (Institute of
Medicine, 2001,
2002). However, beyond just expanding access there are still a
number of
aspects that play into the continuation of disparities. It is not
simply a case
of “build it and they will come.” One aspect in this regard is
ethnic and cul-
tural characteristics that mitigate against accessing traditional
health care
services (Freiman, 1998). Thus, even if access is expanded,
certain groups
may choose not to take advantage of services because of
mistrust, apprehen-
sion, or other reasons. Even more significant though are
programs that are
created and widely used but which may fail to produce desired
outcomes.
In this article, we elaborate on a program evaluation conducted
for the
Health Foundation of Greater Cincinnati (HFGC) of their
MedShare pre-
scription drug subsidy program for indigent populations within
Greater
Cincinnati. The MedShare program had already been shown to
provide
a cost-savings to participants; however, HFGC wanted to further
examine
whether MedShare had an impact on the quality of life of
participants.
MedShare permitted individuals with limited economic
resources greater
access to prescriptions drugs because of reduced costs. In other
words,
8. prescriptions were more readily available to the indigent
population. The
program was so successful that within a few years enrollments
had doubled
and then tripled. However, to our surprise, our initial results
indicated that the
program had little or no impact on the quality of life of
MedShare parti-
cipants. We found that economic necessity and low health
comprehension
contributed to reduced compliance. However, accounting for
compliance,
health improvements were modest at best.
We feel that the results of our analysis point to a more general
problem
in the domain of health care. Many programs are created (e.g.,
Medicare
Part D) to expand access to medical resources. Unfortunately,
just provid-
ing or enhancing access does not necessarily translate into
improved health
care outcomes.
Searching for a Control Group:
Research and Study Design
Because we were evaluating a program that had already been
implemented,
we used a post-test experimental comparison group evaluation
research design
(Bingham & Felbinger, 2002; Spector, 1981). However, because
of the recruit-
ing success of the program, an easily accessible comparison
group was diffi-
cult to identify.
9. 528 Evaluation Review
To overcome this problem, two strategies were employed. The
first
strategy concentrated on what we termed the “internal”
comparison group.
This internal comparison group approach postulated that any
positive
effects of access to pharmaceutical drugs related to quality of
life (particu-
larly for specific disease groupings) would take time to be fully
realized by
MedShare participants. Therefore, newer MedShare enrollees
could be
compared to existing/older MedShare participants to determine
if program-
matic access to pharmaceutical drugs had an effect on
participant quality of
life. We felt that those that had entered the program within the
previous
3 months would constitute new members and were less likely to
have expe-
rienced a significant increase in their quality of life as a result
of access to
drug therapies.
The second strategy focused on creating an external comparison
group
from existing data that had been collected in the 2002
Community Health
Status Survey (CHSS) also conducted in the Greater Cincinnati
area. Using
propensity scores, we matched sample members from the CHSS
10. survey to
those in the MedShare participant survey along 13
characteristics present in
both surveys. The matches using the propensity scores resulted
in a subset
sample from the CHSS survey that mirrored the MedShare
sample on rele-
vant characteristics thereby allowing for comparison between
the two
groups. We then examined quality of life using SF12 physical
and mental
scores using a comparison of means test between the CHSS and
MedShare
subsamples created from the propensity score matches.
Hypotheses
Our primary hypotheses were that MedShare would have a
positive impact
on the mental and physical quality of life of participants. That
is, those indi-
viduals participating in MedShare would possess better (higher)
quality of
life scores, where quality of life is measured using the SF12
physical and
mental scores, than those individuals not in MedShare. This
expectation was
driven by the idea that the application of drug therapies will
have a positive
impact on individual’s lives when they experience health
problems.
H1: MedShare participants should possess significantly higher
quality of life
scores as measured by the SF12 physical component, than those
not partici-
11. pating in MedShare.
H2: MedShare participants should possess significantly higher
quality of life
scores as measured by the SF12 mental component than those
not participat-
ing in MedShare.
Shaw, Carrozza / MedShare and Prescription Drugs 529
In addition to these two main hypotheses, we also had three
more hypothe-
ses related to specific disease categories. The specific disease
categories
under investigation were asthma, diabetes, and hypertension.
We expected
differential benefits from drug therapies related to each of these
three diag-
noses. It is important to note that our expectations are not
necessarily rooted
in a medical understanding of how these drug therapies impact
the disease or
the health of the patient but rather derive from a patient’s self
perception of
the benefit of the drug therapy. From this standpoint, there may
well be a
medication that improves a condition that the patient is
generally oblivious to;
however, because the patient does not see any direct effect from
taking the
medication, the patient is unlikely to perceive any change in
quality of life by
virtue of taking the medication. We feel that for a patient to
report an increase
12. in quality of life because of a drug therapy, there needs to be a
direct symp-
tom(s) that can be linked to the diagnosis and commonly
observable improve-
ments in the condition related to the drug therapy.
Given the acute and episodic nature of asthma and its treatment,
we felt
that access to drug therapies would provide the most direct
relief of symp-
toms for this condition. Consequently, we felt that access to
drug therapies
would contribute to the highest quality of life scores among
asthmatics.
Although not as episodic as asthma, there are a number of
distinct symptoms
related to diabetes such as fatigue, thirst, and consistent voiding
that access
to drug therapies should help to alleviate. Although maybe not
as noticeable
as relief from an asthmatic attack, the alleviation of these
symptoms should
provide for an observable improvement in quality of life. Thus,
we do not
expect to see as much of an improvement in quality of life
among diabetic
participants. Finally, we expect to see little or no effect on
quality of life
among hypertensive participants. For many, hypertension
remains a covert
diagnosis. To be sure, the patient is likely well aware that they
have been
diagnosed with this condition; however, many will possess few
if any day-
to-day or episodic symptoms. As such, drug therapy treatment
of this disease
13. will likely produce few tangible improvements discernable to
the patient.
Consequently, we expect to see either the least amount of
patient observable
improvement or no improvement in quality of life scores among
hyperten-
sive patients, particularly when compared to asthmatic and
diabetic patients.
H3: Participants diagnosed with asthma will have higher quality
of life scores,
as measured by SF12 physical and mental scores, than either
diabetic or
hypertensive patients.
H4: Participants diagnosed with diabetes will have lower
quality of life scores,
as measured by SF12 physical and mental scores, than asthma
patients but
higher quality of life scores than hypertensive patients.
530 Evaluation Review
H5: Participants diagnosed with hypertension will have lower
quality of life
scores, as measured by SF12 physical and mental scores, than
either asthma
or diabetic patients.
Finally, in addition to the question of drug therapies and their
effects on
quality of life, we were also interested in the effects of self-
efficacy and
social support networks on individual perceptions of quality of
14. life. Both of
these characteristics have been shown to have an impact on
quality of life
(Robert, 1999; Schlenk et al, 1997); therefore, we included
scales of each
in our questionnaire. We expected to find effects similar to
previous studies
showing that increased self-efficacy and social support lead to
increases in
perception of quality of life. Thus, we have two final
hypotheses.
H6: Participants scoring higher on the self-efficacy scale would
have higher
quality of life measures than those scoring lower on the self-
efficacy scale.
H7: Participants scoring higher on the social support scale
would have higher
quality of life measures than those scoring lower on the social
support scale.
Finally, our models included controls for gender, age, and race.
These
three factors contribute to health disparities and could account
for differen-
tial outcomes if not included in the models.
Collecting the Data
To measure the impact of the MedShare Program on quality of
life, it
was necessary to conduct a survey of participants. In addition to
informa-
tion on quality of life, HFGC wanted to be able to examine the
data by spe-
15. cific disease groupings including asthma, diabetes, and
hypertension. Even
though participants were indigent, HFGC indicated that
telephone informa-
tion for recipients was generally reliable. A telephone survey
was therefore
deemed to be the best method for data collection.1
The beginning of the data collection coincided with the
implementation
of the Health Information Portability and Accountability Act
privacy rule.
The University of Cincinnati Institutional Review Board issued
a waiver to
allow for the collection of protected health information in the
case of our
survey. The data were collected by telephone survey between
October 10
and November 6, 2003. A randomized listed sample was used to
select
respondents with additional quotas used to ensure adequate
numbers of dis-
ease specific respondents. A total of 928 MedShare participants
responded
to the survey for a margin of error of +/-3.2%.
Shaw, Carrozza / MedShare and Prescription Drugs 531
Measuring Quality of Life, the Dependent Variable
Although there are a number of different indicators for quality
of life
(Bowling, 1997), we chose to focus on the SF12 physical and
mental
16. scores. The reason for selecting the SF12 was threefold:
1. The use of the SF12 would permit us to create the external
comparison
group for the analysis using propensity score matching. Any
other measure
of quality of life would rule out this group for comparison
because they had
not been asked extensive questions regarding quality of life.
2. The SF12 has been shown to be an effective measure that can
be adminis-
tered by telephone, our preferred method of data collection.
3. The SF12 is short and because of resource constraints, we
needed to keep
survey administration costs to a minimum.
Independent Variables
Year of Entry into MedShare Program
This information was essential for the internal comparison. We
wanted to
separate out the most recent enrollees from all others. Initially,
we thought
we would include information regarding each annual cohort to
see if there
were any additional time effects beyond those associated with
the most
recent enrollees. However, the analysis revealed that the
combination of the
various dichotomous variables for year of entry contributed to
abnormal
variance inflation factor scores in the overall models as well as
the disease
17. specific subgroup models. Consequently, because the most
recent enrollees
were the focus of the analysis, we limited the model to just a
dichotomous
variable for entering the program in 2003.2 The constant
therefore contains
information on anyone entering the MedShare program prior to
2003. The
expectation is that participants entering the program in 2003
will have sta-
tistically significant negative coefficients, i.e., they will have
lower quality
of life scores than those participants who have been in the
program for a year
or more. This expectation reflects the premise that access to
these medica-
tions enhances the quality of life of program participants.
Diagnosed with Specific Condition
We asked each participant about six possible conditions:
asthma, dia-
betes, hypertension, back pain, cholesterol, and depression. In
each case, we
asked if a doctor had ever told them they had the particular
condition. We
532 Evaluation Review
then used these conditions as a set of dichotomous variables in
the analysis
with “no condition” being the excluded dummy because it was
possible that
a participant may not have been diagnosed with any of the
18. conditions. It is
this “no condition” that is represented in the constant. Our
expectation was
that each condition would contribute to deteriorating health
with some dif-
ferentiation among the categories. In particular, we conducted
additional
analysis of three of the disease groupings: asthma, diabetes, and
hyperten-
sion. In each case, we ran separate models examining the effects
of the inde-
pendent variables exclusively on the quality of life scores for
individuals
with these three diagnoses. As indicated earlier, we expected
asthma to have
the most pronounced effect, followed by diabetes, and then
hypertension.
Social Support and Personal Agency
We incorporated scales of both social support and personal
agency into
the questionnaire to be able to take account of environmental
and nonmed-
ical factors that may impact estimations of quality of life
(Robert, 1999). In
both cases we expected these scales to have a positive impact on
the qual-
ity of life scores of participants.
Controls: Gender, Age, and Race
Both gender and race are shown to have differential impacts on
health
outcomes because of unequal social, economic, health, and
geographic con-
19. ditions (Shi & Singh, 2004). Consequently, it was important to
include
these variables as controls. Our expectation is that both women
and minori-
ties will exhibit slightly worse quality of life scores than males
and nonmi-
norities. Increased age is also clearly associated with
deteriorating health
outcomes and declining quality of life (Shi & Singh, 2004).
Again, we felt
it was important to include age to fully differentiate the impact
of our other
variables in accounting for the quality of life of MedShare
participants.
Analyzing the Data
The Internal Control Group—Comparing
Recent Enrollees to Existing Enrollees
The Overall Model
The overall model incorporated the entire applicable MedShare
sample
group as well as all of the independent variables. We ran each
model for the
two dependent variables, physical quality of life as measured by
the SF12
physical score and mental quality of life as measured by the
SF12 mental
score. Table 1 presents the findings from each of these models.
Both the
physical and mental models were statistically significant at the
20. 99.9% con-
fidence interval and showed moderate relationships in terms of
the adjusted
R2 estimates. The independent variables explained
approximately 38% of
the variation in the SF12 physical model and approximately
43% of the
variation in the SF12 mental model.
The constant in each of these models reflects the score of a
young,
white, male starting the MedShare program prior to 2003 with
no diagno-
sis of any of the six disease categories. In both cases, the
baseline scores
reflect that MedShare participants are less well off than the
average individ-
ual with scores below the 50% SF12 baseline. Young, white,
male MedShare
participants with none of the specified diseases possess a
statistically sig-
nificant baseline SF12 physical score of 43.3 and a baseline
SF12 mental
score of 33.7. Consequently, MedShare participants start out
below average
in terms of both physical and mental quality of life.
Time of entry into the program had no statistically significant
effect on
either the physical or mental quality of life scores. Our
expectation was that
those individuals joining MedShare in 2003 would have a strong
negative
coefficient indicating that they were considerably less well off
in terms of
quality of life relative to existing MedShare participants.
21. Ultimately, start-
ing the MedShare program in 2003 did not attain statistical
significance in
either model. Consequently, relying on our internal control
group, the null
hypotheses were confirmed as we found no evidence to support
either H1
or H2 that MedShare participants experienced an improved
quality of life,
either physical or mental, relative to non-MedShare
participants.
Impact of Specific Diseases
Having been diagnosed with any of the listed illnesses resulted
in a sta-
tistically significant relationship and a decline in a participants
physical
quality of life score. Chronic back pain contributed to the
greatest decline
in physical health (-9.3). Asthma, diabetes, and hypertension all
contributed
to more than a 3-point decline in the physical score while
depression and
cholesterol reduced the physical score by more than 2 points.
The various
disease categories had considerably less impact on a
participant’s mental
quality of life. The only disease category that mattered here was
whether
one had been diagnosed with depression which resulted in a 9
point reduc-
tion in mental quality of life.
Shaw, Carrozza / MedShare and Prescription Drugs 533
22. 534 Evaluation Review
Table 1
Regression Results Internal MedShare Comparison
SF12 Physical Scorea SF12 Mental Scoreb
B (se) Sig B (se) Sig
Constant 43.34 (2.064) .000*** 33.705 (1.907) .000***
Started MedShare 2003 −0.895 (.760) .240 −0.899 (.703) .184
Respondent told has asthma −3.624 (.894) .000*** −1.156
(.826) .145
Respondent told has diabetes −3.113 (.880) .000*** 0.561
(.813) .473
Respondent told has hypertension −3.557 (.833) .000*** -0.830
(.770) .263
Respondent told has back pain −9.252 (.909) .000*** −0.403
(.840) .618
Respondent told has cholesterol −2.162 (.870) .013* -0.130
(.804) .867
Respondent told has depression −2.396 (.811) .003** −9.081
(.749) .000***
Gender 0.272 (.817) .739 0.506 (.754) .485
Age −0.071 (.030) .017* 0.045 (.028) .090
Race 0.971 (.786) .217 1.577 (.727) .025*
Social support 0.128 (.047) .007** 0.120 (.043) .004**
Personal agency 0.269 (.049) .000*** 0.432 (.045) .000***
Adj. R2 = .384 .000*** .432 .000***
N = 785 779
a. Diagnostics revealed: (a) two outliers; however, they did not
affect the estimates and as they
were real data were kept in the analysis; (b) the variance
23. inflanction factors indicated that mul-
ticollinearity was unlikely; and (c) both the residual vs. fitted
plot and the Breusch-Pagan/
Cook-Weisberg test for heteroskedasticity (.0022) revealed the
presence of heteroskedasticity
in the model. Attempts to correct the heteroskedasticity through
transformations was unsuc-
cessful; however, as heteroskedasticity has a tendency to
underestimate statistical signifi-
cance, we are erring on the side of not identifying an existing
relationship rather than
identifying a relationship that does not really exist (Type 1
error), e.g., a more conservative
estimate (Hamilton, 1992).
b. Diagnostics revealed: (a) six outliers—we opted to remove
these outliers from the analy-
sis because the model without the outliers boosted the overall
adj R2 and affected the
significance of the race variable; (b) the variance inflation
factors indicated that multi-
collinearity was unlikely; and (c) both the residual vs fitted plot
and the Breusch-
Pagan/Cook-Weisberg test for heteroskedasticity (.0000 )
revealed the presence of
heteroskedasticity in the model. Attempts to correct the
heteroskedasticity through trans-
formations was unsuccessful; however, as heteroskedasticity has
a tendency to underesti-
mate statistical significance, we are erring on the side of not
identifying an existing
relationship rather than identifying a relationship that does not
really exist (Type 1 error),
e.g., a more conservative estimate (Hamilton 1992).
* sig at p < .05 level, ** sig at p < .01 level, *** sig at p < .001
level.
24. We expected those with asthma to see the biggest reduction in
quality of
life followed by those with diabetes and then those with
hypertension. The
results show that for the physical quality of life model
hypertension had an
almost similar impact to asthma, and diabetes had the least
impact of the
three. For the mental model, none of the three had an impact on
quality of
life. Consequently, our hypotheses regarding the individual
disease cate-
gories were not confirmed.
Social Support and Self-Efficacy
The scores for social support and personal agency attained
statistical sig-
nificance in the predicted direction for both models allowing us
to reject the
null hypotheses and lending support to our predictions for these
variables.
Social support had a mild positive effect in both models such
that as a
MedShare participant experiences greater feelings of community
and social
bonding within their neighborhood, both their physical and
mental quality
of life tend to improve. Personal agency had a statistically
significant effect
on physical quality of life and a slightly stronger effect on
mental quality
of life. Thus, as MedShare participants’ feelings regarding self-
worth and
25. self-determination increase, their quality of life tends to
improve.
Controls
Finally, age attained significance in the physical quality of life
model,
and race attained significance in the mental quality of life
model. As one
would expect, aging resulted in a declining physical quality of
life score.
Interestingly, the race variable in the mental model indicates
that blacks
possess slightly higher mental quality of life scores than whites.
Gender did
not possess statistically significant effects.
Disease Specific Models: Asthma
Table 2 presents the results for MedShare participants
diagnosed with
asthma. The results indicate the following:
• Both models were statistically significant with the physical
score explaining
22% of the participant’s physical quality of life and 39% of the
participant’s
mental quality of life.
• Time of entry did not have any affect on quality of life in
either model—conse-
quently our primary hypotheses (H1 and H2) to be tested using
the internal
control group were rejected in favor of the null hypotheses.
Shaw, Carrozza / MedShare and Prescription Drugs 535
26. 536 Evaluation Review
• In the physical model only age and personal agency had
statistically signifi-
cant effects. In both cases, they conformed to expectations.
• In the mental model, both social support and personal agency
attained statistical
significance. For both, mental quality of life improves as these
scores improve.
Disease Specific Models: Diabetes
Table 3 presents the results for MedShare participants
diagnosed with
diabetes. The results indicate the following:
• Both models were statistically significant with the physical
score explaining
just 16% of the participant’s physical quality of life and 35% of
the partici-
pant’s mental quality of life.
• Again, time of entry into the MedShare program did not have
any effect on
quality of life.
• In the physical model only age and personal agency had
statistically signifi-
cant effects, and both conformed to expectations.
• In the mental model, age, race, social support, and personal
agency attained
27. statistical significance. The age coefficient shows a somewhat
counterintuitive
Table 2
Regression Results Internal MedShare Comparison,
Asthma Participantsa
SF12 Physical Score SF12 Mental Score
B (se) Sig B (se) Sig
Constant 35.810 (4.770) .000*** 16.717 (4.358) .000***
Started MedShare 2003 −1.243 (1.876) .509 −1.874 (1.714) .276
Gender 0.728 (2.228) .744 3.864 (2.036) .060
Age −0.264 (.066) .000*** .006 (.060) .921
Race .794 (1.920) .680 2.563 (1.755) .146
Social support 0.123 (.113) .278 0.226 (.103) .031*
Personal agency 0.489 (.112) .000*** 0.848 (.103) .000***
Adj. R2 = .217 .000*** .390 .000***
N = 158 158
a. Diagnostics revealed: (a) no outliers; (b) the variance
inflation factors indicated that multi-
collinearity was unlikely; and (c) the Breusch-Pagan/Cook-
Weisberg tests for heteroskedastic-
ity (.6188 and .2041 for the pcs12 and mcs12 models
respectively) were nonsignificant
indicating that heteroskedasticity is not present in either model;
this was confirmed through
examination of the residual versus fitted plots.
* sig at p < .05 level, ** sig at p < .01 level, *** sig at p < .001
level.
28. Shaw, Carrozza / MedShare and Prescription Drugs 537
result in that as age increases, the model indicates a slight
improvement in
mental quality of life. The race coefficient shows that blacks
tend to experience
a better mental quality of life than whites. Again both personal
agency and
social support show positive effects such that as each of these
scores increases,
mental quality of life improves.
Disease Specific Models: Hypertension
Table 4 presents the results for MedShare participants
diagnosed with
hypertension. The results indicate the following:
Table 3
Regression Results Internal MedShare
Comparison, Diabetes Participants
SF12 Physical Scorea SF12 Mental Scoreb
B (se) Sig B (se) Sig
Constant 32.684 (4.832) .000*** 19.509 (3.725) .000***
Started MedShare 2003 .291 (2.063) .888 1.570 (1.590) .325
Gender −1.296 (1.943) .505 −2.842 (1.498) .059
Age −0.191 (.073) .009** 0.167 (.056) .003**
Race 1.786 (1.830) .330 4.749 (1.416) .001**
Social support 0.187 (.103) .071 0.202 (.080) .012*
Personal agency 0.500 (.104) .000*** 0.616 (.080) .000***
Adj. R2 = .164 .000*** .350 .000***
N = 209 208
29. a. Diagnostics revealed: (a) no outliers; (b) the variance
inflation factors indicated that multi-
collinearity was unlikely; and (c) the Breusch-Pagan/Cook-
Weisberg test for heteroskedastic-
ity (.4383) was non-significant indicating that
heteroskedasticity is not present in the model;
this was confirmed in the residual versus fitted plot.
b. Diagnostics revealed: (a) one outlier—removal of the outlier
did not have a major impact on
the individual variables but did affect the overall explanatory
power of the model—given the
amount a single case affected the overall explanatory power of
the model, the analysis was
conducted without the outlier even though it was an actual case;
(b) the variance inflation fac-
tors indicated that multicollinearity was unlikely; and (c) the
Breusch-Pagan/Cook-Weisberg
test for heteroskedasticity (.0923) was nonsignificant indicating
that heteroskedasticity is not
present in the model; the residual versus fitted plot was
examined but was inconclusive, there-
fore we are relying on the results of the Breusch-Pagan/Cook-
Weisberg test as confirmation of
the lack of heteroskedasticity.
* sig at p < .05 level, ** sig at p < .01 level, *** sig at p < .001
level.
538 Evaluation Review
• Both models were statistically significant with the physical
score explaining
just 17% of the participant’s physical quality of life and 31% of
the partici-
30. pant’s mental quality of life.
• Time of entry into the MedShare program again had no affect
on mental
quality of life; however, for physical quality of life there was a
statistically
significant impact in the predicted direction (b = −2.861, p >|t|
= .030).
Unfortunately, this confirmation of our hypothesis occurs in
only one model;
consequently, we cannot conclude that time of entry has a real
impact
because it fails to attain significance in any of the other models.
• In the physical model, time of entry, age, race, social support,
and personal
agency had statistically significant effects. Time of entry has
the anticipated
negative impact. Age had the anticipated effect while the race
coefficient
indicated slightly improved physical quality of life for whites.
Physical qual-
ity of life improved as social support and personal agency
improved.
• In the mental model, age, social support, and personal agency
attained statis-
tical significance. Age again shows a counterintuitive result
such that as age
increases, there is a slight improvement in mental quality of
life. Both per-
sonal agency and social support exhibit positive effects on a
participant’s
mental quality of life.
Table 4
31. Regression Results Internal MedShare Comparison,
Hypertensive Participantsa
SF12 Physical Score SF12 Mental Score
B (se) Sig B (se) Sig
Constant 31.051 (3.331) .000*** 19.233 (2.951) .000***
Started MedShare 2003 −2.861 (1.316) .030* −0.542 (1.166)
.642
Gender −1.643 (1.305) .209 −2.098 (1.156) .070
Age −0.113 (.049) .021* 0.144 (.043) .001***
Race 3.083 (1.232) .013* 1.917 (1.092) .080
Social support 0.204 (.074) .006** 0.162 (.065) .013*
Personal agency 0.450 (.073) .000*** 0.709 (.065) .000***
Adj. R2 = .167 .000*** .312 .000***
N = 390 390
a. Diagnostics revealed: (a) no outliers; (b) the variance
inflation factors indicated that multi-
collinearity was unlikely; and (c) the Breusch-Pagan/Cook-
Weisberg tests for heteroskedastic-
ity (.6538 and .0815 for the pcs12 and mcs12 models
respectively) were nonsignificant
indicating that heteroskedasticity is not present in either model;
this was confirmed through
examination of the residual versus fitted plots.
* sig at p < .05 level, ** sig at p < .01 level, *** sig at p < .001
level.
Shaw, Carrozza / MedShare and Prescription Drugs 539
We now summarize the effects found in examining the internal
32. control
group using the overall model as well as the various disease
specific models.
We found only one significant effect for time of entry into the
program
across all eight models. Thus, generally those who entered the
program most
recently had quality of life scores that were essentially the same
as those
who had been in the program for varying but longer lengths of
time. This
finding disconfirms our hypotheses (H1 and H2) that exposure
to the
program and prescription drugs leads to an improvement in
one’s physical
or mental quality of life.
In terms of the disease specific hypotheses, the overall model
showed
that our expectations regarding asthma, diabetes, and
hypertension were not
supported. There were no effects for these variables in the
overall mental
model. In the overall physical model, these variables did have
an effect but
not in the anticipated ways. Thus, the particular pattern of
expectations
identified in hypotheses H3, H4, and H5 were not supported.
Both social support and personal agency performed as predicted
across
almost all models. Only in the disease specific asthma and
diabetes physi-
cal quality of life models did the social support score fail to
attain statisti-
cal significance. Otherwise both attained statistical significance
33. in the
predicted direction. Thus, two of our ancillary hypotheses (H6
and H7)
were supported.
The External Control Group—Propensity
Score Matching and Comparison With the
Community Health Status Survey
The analysis using propensity score matching focused on
identifying a
group of individuals comparable to MedShare participants on a
number of
relevant characteristics (see Table 5). We used logistic
regression to calcu-
late the propensity scores and thereby facilitate matching on
variables
across different samples (D’Agostino, 1998; Parsons, 2000).
Thus, using
the propensity scores, we were able to identify a comparable
subsample
from the 2002 CHSS conducted in Greater Cincinnati.
Prior to creating and matching the propensity scores, we tested
to see if
there were differences between the MedShare sample and the
overall CHSS
sample. Table 5 shows the results of these tests both before and
after the
propensity score matching. Prior to the analysis, we expected to
find statis-
tically significant differences because the two samples should
have been
dissimilar.
34. 540 Evaluation Review
The data were then combined and the propensity scores obtained
from
the probability estimates using logistic regression. At this point,
the data
were again separated into the two independent samples and
matched
according to the propensity scores. The matching procedures
resulted in a
much lower n size (259) for each group. However, after
matching was com-
plete, retesting the differences in the two samples along the
relevant char-
acteristics resulted in only one statistically significant
difference. Thus, the
matching resulted in a CHSS subsample similar to the MedShare
subsam-
ple across the identified characteristics.
Having obtained two separate but comparable samples, we then
calculated
the SF12 mental and physical scores for both groups. A t-test
for independent
samples was used to determine if there was a statistically
significant differ-
ence between the means of the two groups for each of these
scores. We antic-
ipated a positive statistically significant difference to
demonstrate that
MedShare participants had a different and improved outcome in
terms of
quality of life relative to this comparable group of non-
MedShare partici-
pants. However, we again found no statistically significant
35. differences in
either physical or mental quality of life between these two
groups (see Table
6). We therefore still cannot attribute any impact from the
MedShare program
on a participant’s quality of life.
Table 5
Analysis Using Propensity Score Matching
Prematching Significance Postmatching Significance
Number of children 0.1466 0.5397
Number of adults < .0001 0.3505
Marital status < .0001 0.5881
Employment status < .0001 0.8540
Education < .0001 0.9471
Religion < .0001 0.8274
Race < .0001 0.4490
Chronic back pain < .0001 0.3932
High cholesterol < .0001 0.7743
Asthma < .0001 0.4018
Diabetes < .0001 0.0497*
Depression < .0001 0.5611
Hypertension < .0001 0.7898
MedShare n size 770 259
CHSS n size 1645 259
* sig at p < .05 level.
Shaw, Carrozza / MedShare and Prescription Drugs 541
Initial Results—No Programmatic Effect
36. After comparing both the internal and external control groups to
MedShare participants, we were left with the conclusion that
MedShare
participation, that is, access to dramatically discounted
prescriptions, does
not improve the quality of life of participants. This finding is
not particu-
larly pleasant for at least two reasons. First, although MedShare
has been
shown to provide a financial savings to participants, there is an
expectation
that over time enhanced access to drug therapies should lead to
better health
outcomes as represented in measures such as quality of life.
Second, if
MedShare does not improve participant quality of life, it could
threaten the
existence of the program by convincing donors to withdraw
their financial
support for the program.
In light of our findings, a rational policy perspective would
advocate the
dismantling of the program to reallocate funds to more effective
purposes.
However, the general trend in the use of prescription drugs for
treating ill-
nesses gave us pause in accepting the conclusion that there was
no connec-
tion between MedShare participation and quality of life. We
considered
three possible explanations for this lack of connection.
First, it is possible that we were not using the best quality of
life mea-
sure. Unfortunately, resource constraints coupled with the
37. unique aspects of
identifying appropriate control groups drove the decision to use
the SF12
as the measure of participant quality of life.
Second, it is possible that the overall detrimental environment
in which
MedShare participants find themselves masks any positive
effects that
MedShare participation provides. That is, it may not be possible
for any
measure of quality of life to be sensitive enough to register mild
improve-
ments because of prescription drug access in the face of poverty
and gener-
ally poor living conditions.
Table 6
T-Test for Matched Samples from Propensity Scores
Dependent Variable Study Mean t Sig
PCS12 MedShare 44.62 -1.42 .156
CHSS 45.98
MCS12 MedShare 49.02 0.88 .380
CHSS 48.16
N = 259
542 Evaluation Review
Furthermore, although MedShare participation provides a
savings over
38. the normal cost of prescription drugs, there is still a cost
associated with
acquiring drugs for a MedShare participant. Although the cost
may be
greatly reduced, e.g., paying $10 instead of $40, there is still an
out-of-
pocket expense. According to a rational actor model of
behavior, an indi-
vidual would not turn down such a cost savings given the
potential
importance of the medications. Unfortunately, even at a reduced
cost, the
relative benefit versus cost for these prescriptions is likely
perceived differ-
ently by someone living in poverty and for whom $10 or $15
dollars can
mean the difference in meals or other goods and services that
may be more
immediately attractive than an abstract improvement in health,
particularly
if one’s health conditions are not acute.
Third, compliance could be a factor. Are MedShare participants
taking
their medications as instructed by physicians? Because we asked
about
compliance in the disease specific questions, we turned to an
analysis of
these disease subgroups to try to identify the impact of
compliance.
Could Compliance be a Factor?
For those patients with asthma, diabetes, and hypertension, the
follow-
ing questions were asked:
39. Q1. During the last year, while taking your prescription
medication for
(asthma/diabetes/hypertension), did you always take your
(asthma/diabetes/
hypertension) medications according to your doctor’s
instructions?
Q2. During the last year, did you not receive prescription
medications for
(asthma/diabetes/hypertension) because you needed the money
to buy food,
clothing, or pay for housing?
We found that many MedShare participants were not compliant
either by
virtue of not having taken their medications per instructions or
because they
needed the money for other necessities (see Table 7).
Having identified a pattern of noncompliance, we anticipated
that physi-
cian noncompliance and financial noncompliance did not
overlap com-
pletely. That is, noncompliance overall would be larger than for
either
physician or financial noncompliance separately. The results of
drawing out
these distinctions are displayed in Table 8.
Thus, once overlap among the groups is accounted for,
noncompliance for
participants among all three disease specific groupings is more
than 40%. To
separate out and test for the effects of compliance and
noncompliance on the
40. Shaw, Carrozza / MedShare and Prescription Drugs 543
Table 7
Compliance Among MedShare Participants
No, did not No, did not
Yes, always always take go without Yes, did go
took meds meds per meds for without meds
Patient per physician physician financial for financial
diagnosed instructions instructions reasons in reasons in
with: in past year in past year past year past year
Compliant Noncompliant Compliant Noncompliant
Asthma 75.1 24.9 55.6 44.4
(130) (46) (99) (79)
Diabetes 83.0 17.0 59.7 40.3
(186) (38) (132) (89)
Hypertension 84.8 15.2 62.5 37.5
(368) (66) (275) (165)
Table 8
Compliance Among the Three Disease Groupings
Noncompliant, Noncompliant,
did not did go
always take without Noncompliant,
meds per meds for did not follow
41. Patient physician financial instructions
diagnosed instructions reasons and financial Overall
with: Compliant in past year in past year reasons
Noncompliance
Asthma 43.9 10.4 31.2 14.5 56.1
(76) (18) (54) (25) (97)
Diabetes 52.3 7.2 31.4 9.1 47.7
(115) (16) (69) (20) (105)
Hypertension 56.1 6.0 28.6 9.2 43.9
(243) (26) (124) (40) (190)
models, we created a dichotomous variable. Compliant
participants were
coded 1 and noncompliant participants were coded 0. We then
re-analyzed
the disease specific regression models incorporating the
compliance variable.
Essentially, the primary results were relatively unchanged in the
models such
that Table 9 provides a summary of just the compliance variable
from each
model.
544 Evaluation Review
Table 9 shows that in four of the six models, compliant
participants expe-
rienced statistically significant improvements in their physical
and mental
quality of life. These improvements amounted to a 4-point
increase in mental
42. quality of life for asthmatics; a 3-point increase in mental
quality of life for
diabetics; an almost 4-point increase in physical quality of life
and just over
a 3-point increase in mental quality of life for hypertensive
participants. It is
also possible that there is some measurement error in terms of
compliance.
Cognitive recall over a 12-month period is such that compliance
may demon-
strate an even stronger effect if the error could be reduced.
Ultimately though,
we must be cautious of our interpretation of these findings.
They are related
only to the disease subgroups; they primarily affect the mental
rather than the
physical quality of life; and they are nominal gains at best.
At this point, we feel that the findings regarding MedShare are
mixed. The
evidence presented here points to little or no programmatic
effect on health
outcomes via the MedShare program. Conversely, MedShare has
been very
successful in at least attracting clients and thereby expanding
access to health
services albeit with little impact on health outcomes. There may
however be
a slight positive programmatic effect if compliance is accounted
for; however,
this conclusion requires additional study and the preliminary
evidence from
this study does not hold out hope for strong effects. The results
do however
point to a significant theoretical finding: access is a necessary
but not a suffi-
43. cient step in promoting healthy outcomes.
Table 9
Summary Effects of Compliance Variable in
Disease Specific Regression Models
Physical SF12 Mental SF12
B (se) Sig B (se) Sig
Asthma model: always
takes asthma meds 0.772 (1.904) .686 4.083 (1.766) .022*
Diabetes model: always
takes diabetes meds −0.612 (1.837) .740 3.143 (1.472) .034*
Hypertension model: always
takes hypertension meds 3.689 (1.232) .003** 3.201 (1.083)
.003**
* sig at p < .05 level, ** sig at p < .01 level, *** sig at p < .001
level.
Shaw, Carrozza / MedShare and Prescription Drugs 545
Discussion
As it stands, the current study tends to emphasize that there is
little con-
nection between MedShare and improved quality of life. We
stand by the
utilization of the SF12 as the quality of life measure for the
study because of
44. the program and resource constraints involved; however,
different measures
may provide different insights. It deserves to be noted again
that the SF12
was necessary to allow us to compare the MedShare participants
to similar
individuals surveyed in the CHSS (i.e., our external
comparison). The use of
a different measure would have severely hampered our ability to
make com-
parisons. Also, compliance seems to play into the findings;
however, it is
unclear to what extent compliance is an issue. Where we were
able to exam-
ine compliance, it tends to show up as a statistically significant
issue, but the
strength of its impact in the current study seems minimal at
best. Thus, with-
out further examination, it is difficult to suggest just how
important compli-
ance is to MedShare outcomes. However, the study does point to
important
theoretical issues related to access and the evaluation of health
policies.
We feel that health policymakers tend to suffer from an “if you
build it, they
will come” syndrome. According to this idea, policymakers tend
to inherently
associate increased access with improved health care outcomes.
The logic
underlying this syndrome is sound because access remains a
critically impor-
tant link in the health care policy chain. However, it is not the
last link. Yet,
because in many ways, it is relatively straightforward and
45. discernable (con-
structing new facilities and programs, providing subsidies for
patients), we
overemphasize our policy efforts around access. Similar to other
policy
domains though, measuring outcomes and determining impact
are much more
difficult and less politically sexy; however, it is at this
postaccess stage that we
need to concentrate more effort than we previously have. Our
current study
highlights how easy it is to see outward signs of programmatic
success and fall
into the assumption that a program is therefore having the
anticipated impact.
If there had been no desire to quantify this perceived success to
attempt to
acquire additional funding, this study would likely never have
been conducted
and policymakers would continue to assume a nexus between
MedShare and
improved community health outcomes. These findings however
illustrate that
(a) it is important not to fall into the “if you build it, they will
come” syndrome
and ignore postaccess issues and (b) it is vital to devote
resources and efforts
to program evaluation to gauge the broader impact of any given
policy.
Notes
1. Although in-person surveys would have been the optimal
solution there were a number
of mitigating factors including cost, coverage, availability of
clinic personnel, and comparability
46. 546 Evaluation Review
with the Community Health Status Survey that caused us to
focus our attention on a tele-
phone survey.
2. We considered whether to use an ordinal time scale (e.g., 1, 2
. . . 5) for year of entry or
a set of dichotomous variables. After testing for curvilinearity,
we opted to utilize dichotomous
variables to account for year of entry into the program (Hardy,
1993).
References
Anderson, R. (1995). Revisiting the behavioral model and
access to care: Does it matter?
Journal of Health and Social Behavior, 36, 1–10.
Bingham, R. D., & Felbinger, C. L. (2002). Evaluation in
practice (2nd ed.). New York: Seven
Bridges Press.
Bowling, A. (1997). Measuring health, a review of quality of
life measurement scales.
Philadelphia: Open University Press.
D’Agostino, R. B. (1998). Tutorial in biostatistics, propensity
score methods for bias reduc-
tion in the comparison of a treatment to a non-randomized
control group. Statistics in
Medicine, 17, 2265–2281.
47. Ensor, T., & Cooper, S. (2004). Overcoming barriers to health
service access: Influencing the
demand side. Health Policy and Planning, 19, 69–79.
Freiman, M. P. (1998). The demand for health care among
racial/ethnic subpopulations.
Health Services Research, 34 (1, Part II), 215–227.
Hamilton, L. C. (1992). Regression with graphics. Belmont, CA:
Doxbury Press.
Hardy, M. (1993). Regression with dummy variables. Thousand
Oaks, CA: Sage.
Institute of Medicine. (2001). Coverage matters, insurance and
health care. Washington, DC:
National Academies Press.
Institute of Medicine. (2002). Unequal treatment: Confronting
racial and ethnic disparities in
health. Washington, DC: National Academies Press.
Parsons, L. S. (2000). Using SAS software to perform a case-
control match on propensity score
in an observational study. Poster 225–25 presented at the SAS
Users Group International
Conference in Indianapolis, IN.
Robert, S. (1999). Socioeconomic position and health: The
independent contribution of com-
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Schlenk, E. A., Erlen, J. A., Dunbar-Jacob, J., McDowell, J.,
Engberg, S., Sereika, S. M., et al.
(1997). Health-related quality of life in chronic disorders: A
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Shi, L., & Singh, D. A. (2004). Delivering health care in
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Sage.
Thomas Shaw is an assistant professor at the University of
South Alabama where he
teaches courses in political science and public administration
with a focus on policy analysis
and evaluation.
Mark Carrozza is a senior research associate at the Institute for
Policy Research and direc-
tor of the Southwest Ohio Regional Data Center, Institute for
Policy Research, Cincinnati.
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79. questions or issues. If, in your opinion, your article does not
address one or more of them, then you should say so and defend
or justify why you believe that to be the case. In all cases,
defend or justify your answers with specific references to
words, phrases, or passages in the article and corresponding
references to the assigned readings for the course. In other
words, while your critique should be written in your own words
(third person only), you do need to make references to specific
words, phrases, and relevant passages in the text of your article,
as well as corresponding references to the assigned readings for
the course. Each of the following items in BOLD must be listed
as a heading on your critique. Do not use bullet points. Discuss
in your critique the questions/issues under each bolded item. If
a question/issue does not apply, please explain why.
• _Summary (1 page)
o Summarize the contents of your article.
o Who wanted the policy or program evaluated? In other words,
why did the author(s) conduct the evaluation?
• _E_v_a_l_u_a_t_i_o_n_ _G_o_a_l_(_s_)_ _(_1_ _p_a_g_e_)_
_
o What were the goals or desired outcomes of the policy or
program under examination?
o How did the author(s) propose to evaluate the policy or
program in terms of its goals or outcomes?
• _Theoretical Perspective (2-3 paragraphs) mus be 1 page
o What theory connects the policy or program to the goals or
outcomes?
o What rival or plausible explanations or theories were ruled
out?
o How did the theory guide the author(s) in their conduct of the
evaluation?
• _Research Methods (2-3 paragraphs) 1 page
o What principal research method(s) did the author(s) use in the
conduct of the evaluation?
Did any unintended or unanticipated consequences arise? If so,
what were they?
80. o How and why did they arise?
• _Conclusion (1 page)
o What did the author(s) conclude? On what basis?
o Did the policy or program achieve its intended goals or
outcomes in whole, in part, or not at all? If so, why or why not?
o To what uses were the results of the evaluation to be put?
o Based on the results of the evaluation, what principal
recommendations did the author(s) make?
• _Evaluation Concepts (1-2 pages)
o After you have written your summary, identify which of the
following apply to the article. Be sure to define the concept and
explain how each of your selections applies to your article.
Implementation evaluation
Process evaluation
Outcome evaluation
Goals or desired outcomes
Program theory
Quantitative methodology
Qualitative methodology
Dependent variable(s)
Independent variable(s)
Experimental design
Quasi-experimental design
Internal validity
External validity
Unit of analysis
Replication
Meta-analysis
Cost-benefit analysis
Cost-effectiveness analysis