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Development of an instrument to measure seniors’ patient safety
health beliefs: The Seniors Empowerment and Advocacy
in Patient Safety (SEAPS) survey§
Nancy C. Elder a,*, Saundra L. Regan a,1, Harini Pallerla a,1,Linda Levin b,2, Douglas Post c,3, Donald J. Cegela d,4
a Department of Family Medicine, University of Cincinnati, Cincinnati, United Statesb Department of Environmental Health, University of Cincinnati, Cincinnati, United States
c Department of Family Medicine, Ohio State University, Columbus, United Statesd School of Communication and Department of Family Medicine, Ohio State University, Columbus, United States
Received 20 February 2007; received in revised form 18 July 2007; accepted 22 July 2007
www.elsevier.com/locate/pateducou
Patient Education and Counseling 69 (2007) 100–107
Abstract
Objective: To develop a survey to measure seniors’ embracement of ambulatory patient safety self-advocacy behaviors, the Senior Empowerment
and Advocacy in Patient Safety (SEAPS) survey.
Methods: Content was developed by review of published recommendations combined with interviews and focus groups with community
members; items were generated for subscales based on the health belief model (HBM). Psychometric characteristics were assessed by cluster and
correlation analyses on a pilot test of 143 community dwelling seniors; the ability of the subscales and demographic variables to predict reported
behavior was investigated by multiple regression.
Results: The four subscales of the SEAPS were outcome efficacy (OE), attitudes (ATT), self-efficacy (SE) and behaviors (BEH). Cronbach alphas
were 0.74 for ATT, 0.79 for BEH, and 0.91 for OE and SE. Analysis of variance showed that there were no differences in any subscale score by race,
education level or frequency of doctor visits, but women were noted to have significantly higher scores ( p < .01) on the ATT and SE subscales and
for the total of all the scales. Multiple regressions showed that SE significantly predicted self-reported behavior ( p < .001). OE was a significant
predictor for whites ( p < .001) but not for African-Americans ( p = .24).
Conclusions: We have developed a short, 21-item self-administered survey to assess seniors’ views about their participation in safety tasks.
Practice implications: We believe the SEAPS shows promise to be a tool for evaluating interventions and training programs aimed at improving
seniors’ self-advocacy skills. Effective interventions may improve the involvement of patients in their own safety in the clinical setting.
# 2007 Elsevier Ireland Ltd. All rights reserved.
Keywords: Patient safety; Patient communication; Health belief; Ambulatory care
§ This work was supported by a Dean’s Discovery Grant from the College of
edicine, University of Cincinnati.
* Corresponding author at: Department of Family Medicine, University of
incinnati, PO Box 670582, Cincinnati, OH 45267-0582, United States.
el.: +1 513 558 1430; fax: +1 513 558 3266.
E-mail addresses: [email protected] (N.C. Elder),
[email protected] (S.L. Regan), [email protected] (H. Pallerla),
[email protected] (L. Levin), [email protected] (D. Post),
[email protected] (D.J. Cegela).1 Tel.: +1 513 558 1430.2 Tel.: +1 513 558 4838.3 Tel.: +1 614 293 8087.4 Tel.: +1 614 292 3675.
738-3991/$ – see front matter # 2007 Elsevier Ireland Ltd. All rights reserved.
oi:10.1016/j.pec.2007.07.007
1. Introduction
Utilization of strategies to increase patients’ involvement in
their health care can improve health outcomes [1]. Teaching
patients to effectively communicate with clinicians can
enhance patient participation. Findings suggest that patient
communication training improves important clinical indicators,
including chronic disease outcomes, functional status and
adherence to treatment recommendations [2–4].
Patient safety, the freedom from accidental injury in health
care, has also become an issue of importance for patients [5–7].
Recommendations for patients to be more involved in the safety
of their own care [8–10] have been utilized by patients to varying
N.C. Elder et al. / Patient Education and Counseling 69 (2007) 100–107 101
degrees [5,7,11]. In the ambulatory setting, where most health
care in America takes place [12], patients’ activities can
influence clinical outcomes, and this has been postulated for
patient safety, too [13]. For this reason, recommendations to
patients flow freely [8,9] and educators and researchers are
developing interventions to assist patients in understanding and
participating in safety oriented health behaviors [14]. However,
evaluating the effectiveness of such interventions can be difficult.
There are several theories that try to explain the health
behaviors of individuals [15]. The health belief model (HBM)
[16,17] posits that several variables are necessary prior to
individuals participating in specific health behaviors. These
include being able to overcome the barriers and perceiving
benefit from the recommended behaviors as well as having
confidence in one’s ability to effectively take action [7,17]. This
last point refers to having self-efficacy, which several authors
have added to the original HBM [7,18–20].
As patients are being instructed to take a more active role in
improving the safety of their ambulatory care, there is a need to
objectively evaluate their activities. Seniors are especially at
risk for errors and safety lapses, both because they use more
health care and because they are less likely to participate in
active medical decision making [21–24]. However, it is not
even clear what the most important safety behaviors are for
elderly patients in the ambulatory setting, as most published
safety recommendations have been developed with little patient
input and focus mainly on hospital care [14]. While some
programs have tried training patients to follow some published
safety recommendations, their evaluation methods are often site
specific and not applicable to large groups of patients [13].
Since seniors are the largest consumers of health care [25], we
designed a survey based on the HBM geared to this population,
developed with patient as well as expert input and focused on
safety issues of importance in the ambulatory setting. We
believe such an instrument could supplement difficult to
measure objective outcomes of patient focused ambulatory
safety interventions and education programs [26]. This paper
describes the development and pilot testing of a survey
instrument to measure seniors’ participation and embracement
of ambulatory patient safety self-advocacy behaviors.
2. Methods
To develop the Seniors Empowerment and Advocacy for
Patient Safety (SEAPS) survey, we used a multi-step process to
address both content and construct validity. We elected to focus
on three subscales:
� O
utcome efficacy (OE) the belief that the actions will be abenefit to one’s health,
� A
ttitudes (ATT) concerns about barriers to participating in theactions,
� S
Fig. 1. Steps in the development of the SEAPS survey.
elf-efficacy (SE) confidence in one’s ability to effectively
take action.
We also included a self-report proxy for the performance of
actions called behaviors (BEH). This allows predictors of
behavior to be determined and also provides the ability to assess
self-reported changes in patient safety behaviors which may
result from future interventions. This research received approval
from the University of Cincinnati Institutional Review Board.
2.1. Item generation and selection
Since there is not a ‘‘gold standard’’ for patient self-
advocacy behaviors for ambulatory care, it was necessary for us
to develop the content from multiple sources in order to achieve
content validity (Fig. 1).
2.1.1. Initial safety recommendations
In a previous study, we interviewed 24 primary care patients
who had experienced medical error or preventable problems in
their health care; these patients described what health care
behaviors they now used to improve their chances for quality
and safe care [5]. At this same time, we reviewed published
recommendations from organizations such as the National
Patient Safety Foundation (NPSF) [27], the Agency for
Healthcare Quality and Safety (AHRQ) [8], the Joint
N.C. Elder et al. / Patient Education and Counseling 69 (2007) 100–107102
Commission for the Accreditation of Healthcare Organizations
(JCAHO) [9] and others [10,28,29]. In addition, to ensure that
the recommendations to patients were applicable to important
medical problems, we also reviewed the medical literature
describing common errors and problems in primary care
[26,30–32]. Combining these sources, we collated an initial list
of safety recommendations applicable to ambulatory care.
2.1.2. Final safety recommendations
In order to solicit further patient input into the recommenda-
tions, we conducted eight focus groups with volunteers from
the community. Participants were solicited for these groups
through flyers posted in primary care physicians’ offices, in
community, senior and recreation centers and on community
bulletin boards around town. All ages of adults were invited,
and 79 people (mean age = 45, range 20–78, 18% over age 60)
participated and discussed their experiences with using the
recommendations. From this information the list of safety
recommendations for patients was refined, and after additional
medical literature review, we collated a final list of 10 primary
care safety recommendations (Table 1).
2.1.3. Development of SEAPS 40 question draft
The recommendations were formulated into questions for
the subscales adapted from the health belief model: outcome
efficacy, attitudes, self-efficacy and behaviors. The questions
were then reviewed via e-mail by a small group of primary care,
patient safety and patient education researchers and advocates.
2.2. Psychometrics
2.2.1. Pretest
The SEAPS 40 was pretested on two groups of independent
living elderly individuals. Participants completed the survey
and participated in a ‘‘think aloud’’ focus group discussion
about the survey. Group 1 (nine African-American women,
mean age 75.4 years) met at an urban senior center, while group
2 (eight white women and four white men, mean age 84.7) met
Table 1
Initial safety recommendations for patients
Patients educate themselves on their own health concerns and medications
Patients give providers a complete and thorough story of their health
problems and concerns
Patients ask questions of their providers until they understand medical
diagnoses, medications prescribed, tests ordered and future plans
Patients speak up about problems with office staff and office procedures
that interfere with the ability to access care
Patients keep a list of their medications, dietary supplements and drug
allergies
Patients double check medication prescriptions at the pharmacy to assure
they have received the name and doses of the medicine they expect
Patients double check on the results of laboratory or diagnostic testing to
assure they have received results for all tests done
Patients access a second opinion when needed
Patients are willing to change providers if they feel health concerns are
not being met
Patients bring a friend or family member to doctors’ visits and for
procedures and tests to serve as an advocate and helper
at a suburban retirement community. The average time to
complete the survey was 12.4 min for group 1 and 13.8 min for
group 2. After review of participant comments and survey
results, specific changes made at this point included re-wording
of the outcome efficacy questions and simplification of
language.
2.2.2. Pilot test
The SEAPS 40 was then pilot tested with a sample of
community dwelling elderly to assess baseline response
characteristics, item reproducibility and to decrease the total
number of questions in the survey.
2.2.2.1. Cohort description. Participants were solicited at
multiple locations to insure a range of ages, race (white and
African-American (AA)) and socioeconomic classes. These
locations included waiting rooms in the University Family
Medicine health centers, senior centers, and social functions at
several churches. Inclusion criteria included age over 59, living
independently and English speaking. Exclusion criteria
included significant cognitive impairment. For those with
low literacy or visual problems, the survey was read by research
assistants.
Two hundred seniors were approached and 150 (75%)
completed the survey, 50 (25%) refused; of the 150, 143
answered enough questions to be used for analysis. All
participants were recruited in the Cincinnati metropolitan area
(urban and suburban). The mean age of the participants was 70
(range 60–97) years, 71% were female, 62% were white, and
73% were at least high school graduates. Over 99% of the
participants had medical coverage (Medicare, Medicaid,
private insurance or a combination) and visited a doctor at
least once a year.
2.2.2.2. Descriptive statistics, survey accuracy and reliabili-
ty. Each survey question was answered on a Likert-type scale
from zero to three. Negatively worded questions were reverse
coded, so that three indicated increased self-efficacy, increased
outcome efficacy, positive attitudes and more frequent
behaviors. Missing responses were replaced by the subject’s
modal response to answered questions within the same
subscale. We calculated the mean and standard deviation of
each question across subjects to compare averages and ranges.
Subscale means for all subjects were calculated by averaging
question responses for each subject, then averaging the
subject-specific means across subjects. Since a normal
approximation to the distribution of subscale means was
reasonable, we calculated the percent accuracy of each
subscale as 1.96 times the standard error of the subscale
average (or one-half the width of the 95% confidence interval
of the mean). We used Cronbach alphas to estimate the
reliability of questions for each subscale.
Subscale accuracies ranged from 4.6 to 6.0%, indicating that
95% of the time the error of measurement (difference between
observed and true subscale means) was within 4.6–6.0% of the
observed subscale mean. Cronbach alphas were 0.74 for ATT,
0.79 for BEH, and 0.91 for OE and SE.
N.C. Elder et al. / Patient Education and Counseling 69 (2007) 100–107 103
2.2.2.3. Questionnaire reduction. Subscale-specific question
reduction was implemented by performing cluster analyses in
which clusters of questions were developed that could be
represented by a single variable. Seventy-five percent of the
variability of all questions in each subscale was explained by
five and/or six clusters. These analyses were followed by
hierarchical cluster analyses, involving nine stages (the
number of original questions minus one) in which the two
‘‘nearest’’ questions or clusters of questions were combined
sequentially based on the Spearman similarity index as a
distance measure representing clustering. Clusters were
similar to those obtained by the initial analyses. Graphical
representations of hierarchical clusters were obtained that
facilitated the selection of questions which represented the
original sets of clusters. Medians (minimum, maximum)
values of the resulting (nine) distances indices for each
subscale were: ATT 0.10 (0.00, 0.34), OE 0.37 (0.05, 0.51),
BEL 0.26 (0.06, 0.45), and BEH 0.13 (0.01, 0.45). Cluster
analyses were performed using the SAS procedure PROC
VARCLUS and the S-PLUS function hclust.
Clustering of questions on each subscale was also assessed
by race and gender, since a priori it was judged important to
keep those questions that contained information specific to
race and gender differences. Based on the magnitude of
Spearman indices across race–gender subgroups, with
priority given to the results for all subjects, three different
subsets of five or six questions were selected from each
subscale. The final subset of questions from each subscale
Table 2
Means for 21-question final SEAPS for health belief model subscales and total SE
Category of (N) Means and 95% confidence intervals
BEH ATT
Gender
Female (101) 1.6 (1.5, 1.8) 2.0 (1.9, 2.1)
Male (42) 1.4 (1.3, 1.6) 1.7 (1.5, 1.9)
p-Value .06 <.01
Race
AA (50) 1.7 (1.5, 1.8) 1.9 (1.8, 2.1)
White (93) 1.5 (1.4, 1.7) 1.9 (1.7, 2.0)
p-Value .25 .65
Education
<HS (39) 1.5 (1.3, 1.7) 1.9 (1.7, 2.1)
HS (53) 1.6 (1.4, 1.7) 1.8 (1.7, 2.0)
<College (25) 1.8 (1.5, 2.0) 2.0 (1.8, 2.3)
College (26) 1.6 (1.4, 1.9) 1.9 (1.7, 2.1)
p-Value .25 .54
Doctor office visits
Occas. (100) 1.6 (1.5, 1.7) 1.9 (1.8, 2.0)
Frequent (43) 1.6 (1.4, 1.8) 1.9 (1.7, 2.0)
p-Value .71 .90
All (143) 1.6 (1.5, 1.7) 1.9 (1.8, 2.0)
Means, 95% confidence intervals and p-values by analysis of variance. N = num
SE = self-efficacy subscales; A = African-American; HS = High school; Occas. = s
was chosen as follows: race–gender means by subscale were
calculated for each subset of questions. The subset of
questions from each subscale whose means were on average
closest to the race–gender means calculated from all 10
questions was chosen for the analyses. Spearman correlations
between subscale means ranged from 0.4 to 0.6. The
correlations between BEH and the other subscales were:
ATT 0.42, SE 0.38, and OE 0.51.
2.2.2.4. Comparison of original SEAPS 40 question survey
and final SEAPS. Nine of the original 10 safety recommenda-
tions for patients remained in the final SEAPS. The
recommendation for patients double checking their prescrip-
tions from the pharmacy was removed for two reasons—in all
subcategories, it received the highest mean scores from survey
participants, indicating that they already were comfortable with
and doing this activity, and published medical literature noted
that elderly patients considered this activity to be an
unnecessary recommendation [25].
For each subscale, we performed analysis of variance of the
final SEAPS data to test differences between race, gender,
education level, and frequency of doctor visits. Although not
shown, these analyses were also performed for the original
SEAPS 40 question survey. We tested the equality of means
between the two versions of the survey by judging the overlap
of 95% confidence intervals. Both subscale averages and 95%
confidence intervals for the SEAPS 40 were in good agreement
with the final SEAPS survey.
APS score by demographic characteristics
OE SE Total
1.9 (1.8, 2.1) 2.3 (2.1, 2.4) 1.9 (1.8, 2.0)
1.8 (1.6, 1.9) 1.9 (1.7, 2.1) 1.7 (1.6, 1.9)
.36 .01 <.01
2.0 (1.8, 2.2) 2.2 (2.0, 2.4) 1.9 (1.8, 2.0)
1.9 (1.8, 2.0) 2.1 (2.0, 2.3) 1.8 (1.7, 1.9)
.37 .65 .33
2.0 (1.8, 2.2) 2.0 (1.8, 2.3) 1.8 (1.7, 2.0)
1.8 (1.7, 2.0) 2.1 (1.9, 2.3) 1.8 (1.7, 2.0)
2.0 (1.7, 2.2) 2.4 (2.2, 2.6) 2.0 (1.8, 2.2)
1.9 (1.7, 2.2) 2.2 (2.0,2.5) 1.9 (1.7, 2.1)
.67 .25 .35
1.9 (1.8, 2.0) 2.2 (2.0, 2.3) 1.9 (1.8, 2.0)
2.0 (1.8, 2.2) 2.2 (2.0, 2.4) 1.9 (1.8, 2.0)
.18 .97 .59
1.9 (1.8, 2.0) 2.2 (2.0, 2.3) 1.9 (1.8, 2.0)
ber of subjects; ATT = attitudes; BEH = behaviors; OE = outcome efficacy;
everal times a year or less; Frequent = once a month or more.
Table 3
Multiple regression analysis of the association between health belief model subscales and demographic characteristics as independent variables, and self-reported
behavior as dependent variable
Predictor variable Magnitude and direction
of effect of predictor variable on BEHa
p-Value measuring significance
of predictor variable
Percent model variability
explained by predictor variable
SE +0.3 <.001 22.7
OE
White +0.4 <.001 28.3
AA �0.1 .24 5.3
ATT +0.1 .15 4.1
Race: AA vs. white +1.1 <.001 32.9
Gender: female vs. male +0.02 .79 0.1
Education: <HS vs. �HS �0.2 .08 6.3
Frequency of office visits: occ. vs. freq. +0.04 .70 0.3
AA = African-American;<HS = less than high school graduation;�HS = high school graduation or greater; occ. = several times a year or less; freq. = once a month
or more.a For SE, OE, and ATT the magnitude of the effect indicates the change in behavior score when the independent variable increases by +1 unit. The effect of OE
differs by race; there were no significant modifying effects of demographic variables for any other subscales. For demographic variables the magnitude of the effect
indicates the difference in mean behavior of subjects in the first category compared to the second category (e.g. AA compared to white or female compared to male).
N.C. Elder et al. / Patient Education and Counseling 69 (2007) 100–107104
2.2.3. Correlation of behavior and other subscales
in the final SEAPS
We determined the ability to predict BEH from other
subscale scores, adjusted for race, gender, education and
frequency of doctor visits by multiple regression analysis.
Regression coefficients were interpreted by calculating their
effect on BEH when a subscale score increased by 1 unit, or
when a categorical variable was equal to 1 (white, male, high
school graduate or greater, frequent doctor visits) versus 0
(African-American, female, less than high school graduation or
occasional doctor visits), controlling for other variables. The
contribution to the variability of BEH scores that was explained
by the model (R2) and the percents of the variability of BEH
explained by each predictor variable were obtained.
In order to investigate the robustness of the regression
analysis for predicting behavior from the other (correlated)
subscale measurements (r = 0.42–0.51), the analysis was
repeated by replacing the three subscale measurements of
each subject by (i) a single variable equal to the first principal
component (PC), which explained 68% of the variation of
subscale measurements, and (ii) by the first two principal
components which explained 90% of the variability. The same
covariates, i.e. race, gender, education, and frequency of
doctor visits were included in each model. The coefficients of
determination (R2) were 0.39, 0.36, and 0.38 when all
subscales, the first PC, and the first two PCs were modeled,
respectively. Predicted values of behavior were almost
identical for the PC analyses and the original analysis. All
analyses were performed using SAS and S-Plus statistical
software.
3. Results
Table 2 shows the total and subscale averages and 95%
confidence intervals for the final SEAPS survey (see
Appendix A). Analysis of variance revealed that there were
no differences in any subscale score by race, education level or
frequency of physician visits, but women were noted to have
significantly higher scores on the ATT and SE scales.
We analyzed the ability of the HBM subscales and
demographic variables to predict self-reported behavior using
multiple regression (Table 3). The SE subscale, measuring an
individual’s confidence they could do a task, was positively and
significantly predictive of self-reported behavior. Race was
found to be a predictor of behavior both in that it modified the
effect of outcome efficacy (which was a significant predictor for
whites but not for African-Americans) and also as an
independent predictor of behavior. The attitude scale, while
positively associated with behavior, was not significant.
Gender, education and frequency of doctor visits were not
significantly related to self-reported behavior.
4. Discussion and conclusion
4.1. Discussion
From the care of chronic health problems to preventing
disease, many patients are taking an active role in their own health
care, and generally benefiting from such [23,33]. Following this
trend, patients in America today are also being called on to be
active participants in their own safety [8–10,34,35]. Yet the few
studies that measure patients’ participation in safety tasks show
mixed results [6,25,36], and these focus primarily on the hospital
setting. There are no validated instruments to assess patients’
beliefs about participating in safety activities. We have created
the Seniors Empowerment and Advocacy in Patient Safety
survey, a short self-administered survey that can assess elderly
persons’ beliefs about being their own patient safety advocate in
ambulatory care. We believe the strength of this survey lies in the
rigor with which we generated the initial questions using a multi-
step process with active patient input. The demographic
correlations we report are consistent with research in similar
areas, such as participation in medical decision making [23,37–
39], adding to the strength of our survey. Thus, along with
N.C. Elder et al. / Patient Education and Counseling 69 (2007) 100–107 105
objective measures of patient behavior, this survey can assist
researchers and educators in assessing patients’ beliefs and
attitudes, and allow assessments of changes after interventions.
Two weaknesses of prior recommendations to patients about
how to improve their own safety are that they were developed
with little direct patient input and have a strong focus on
hospital care [14]. However, most medical care in America is
provided in physicians’ offices, especially primary care offices,
and not hospitals [12]. We used both published recommenda-
tions and direct patient input to develop our survey content, and
focused it on ambulatory care. A series of individual interviews
[5], focus groups and pretesting ensured that the content of our
SEAPS survey was strongly grounded in the experience of
patients, especially elderly patients. Moving patient safety
research out of the hospital to the community is an important
step in improving medical quality and safety [40].
Our survey shows applicability for both African-Americans
and whites, as well as both genders and all levels of educational
attainment and frequency of doctor visits. While we did find
significant differences between women and men in their
attitudes and perceptions of self-efficacy on univariate analysis,
this gender finding is consistent with research on active
decision making and participation in health care, in which
women are regularly found to be more active participants in
health care [23,24,33,38,39].
The three HBM subscales were all positively predictive of
self-reported behavior, although only self-efficacy was sig-
nificant for all participants, consistent with other research on
the HBM and patient safety [6,7]. Race was a significant
modifier for outcome efficacy and was also an independent
predictor of behavior. This is consistent with research showing
the decision to be an active participant in health care is complex
and depends on the interplay of personal, physician and
contextual factors [23,24,37,38,41]. Our findings suggest that
comfort with certain tasks and self-confidence in one’s ability
to perform a task may account for some of these differences
[7,17]. The exact role race, education, gender and illness burden
play when elderly people make decisions about health care is
still uncertain. In published studies, demographic and situa-
tional factors alone explain 20% or less of the variability of
patient preferences in decision making [23].
Appendix A. Final SEAPS survey
How often do you do the following tasks? (behaviors) I never
do this
1. How often do you teach yourself about
your own health problems and medicines?
&
2. How often do you ask your doctors questions
about your health problems, lab tests and medicines?
&
3. How often do you keep an updated a list of all your
medicines, including those from the drug store and
health food store?
&
4. How often do you call the doctor’s office if you
haven’t received the results of laboratory
or X-ray tests?
&
5. How often do you get a second opinion from
another doctor when needed?
&
There are limitations to our survey development. Our
population consisted only of urban and suburban white and
African-American participants in a Midwest City. Other races
or ethnic groups, or those in other regions of the country may
have differences that would have affected our psychometrics or
item selection. As with all surveys asking about self-reported
behaviors, we have no objective corroboration that our subscale
BEH accurately reports what patients really do. However, it was
positively correlated to our other subscales, and we encourage
the use of objective measures along with the SEAPS in future
work. The survey is written at the 8th–9th grade level, which
may be limiting for those of low literacy; however the option to
read it aloud and explain questions is possible. A limiting factor
to the detection of question and subject differences is the small
range of responses of the four-point Likert scale. Continuously
measured responses would have allowed more sensitive
statistical analyses to have been performed.
4.2. Conclusions
Elderly patients are being asked be active participants in
patient safety in the ambulatory setting [14,34,35]. Early
research and anecdotal reports note that there are patients of
all ages who now incorporate patient safety self-advocacy
activities, and more research is ongoing [5,34,35,42]. We have
developed a short, 21-item self-administered survey, the Seniors
Empowerment and Advocacy in Patient Safety (SEAPS) survey
to assess elderly patients’views about their participation in safety
tasks. We used both published recommendations and direct
patient experience in its development, and applied rigorous
psychometric analyses to question selection.
4.3. Practice implications
We believe the SEAPS survey, along with objective measures
of participation and outcome can be used by researchers and
educators to assess interventions and training programs for
patients about being active in their own ambulatory medical care
safety. Effective interventions could increase patients’ safety
advocacy skills and abilities and, ultimately, lead to their
increased use by patients in the practice setting.
I rarely
do this
I often
do this
I always
do this
& & &
& & &
& & &
& & &
& & &
Appendix A (Continued )
6. How often do you ask a friend or family member
to come with you to doctors’ visits?
& & & &
How confident are you that you could actually do
the following tasks? (self-efficacy)
I don’t think
I could
I doubt
I could
I probably
could
I definitely
could
1. How confident are you that you could teach
yourself about your own health problems
and medicines?
& & & &
2. How confident are you that you could call
the doctor’s office if you haven’t received
the results of laboratory or X-ray tests?
& & & &
3. How confident are you that you could get
a second opinion from another doctor if
you think it is needed?
& & & &
4. How confident are you that you could give your
doctors a complete and thorough story of your
health problems?
& & & &
5. How confident are you that you could ask a friend
or family member to come with you to doctors’ visits?
& & & &
How much do you agree or disagree with each
statement? (attitudes)
Strongly
disagree
Disagree Agree Strongly
agree
1. I feel comfortable changing doctors if I think my
health concerns are not being met
& & & &
2. I feel it is difficult to ask a friend or family member
to come with me to doctors’ visits
& & & &
3. It would be hard to ask my doctors a lot of
questions about my health problems, lab tests
and medicines
& & & &
4. I would feel comfortable calling the doctor’s office
if I haven’t received the results of laboratory
or X-ray tests
& & & &
5. It would be easy to keep an updated list of all my
medicines, including those from the drug store
and health food store
& & & &
How strongly do you believe that doing the following
will improve your health? (outcome efficacy)
Strongly
do not believe
Do not
believe
Believe Strongly
believe
1. How strongly do you believe that giving your doctors a complete and thorough story of your
health problems and concerns will improve your health?
& & & &
2. How strongly do you believe that complaining in writing if you have problems with office
staff or doctor will improve your overall health?
& & & &
3. How strongly do you believe that getting a second opinion when needed will improve your health? & & & &4. How strongly do you believe that asking a friend or family member to come with you to
doctors’ visits will improve your overall health?
& & & &
5. How strongly do you believe that teaching yourself about your own health problems and medicines
will improve your overall health?
& & & &
N.C. Elder et al. / Patient Education and Counseling 69 (2007) 100–107106
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