Term 74

 Society Perspective 

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This assignment should lead to the term paper after the student has read the article carefully and entirely.  Use the following questions to guide your critique.

Term Paper

  1. Does the title clearly indicate what the research is about? Can you relate it to DM through the title? Explain.
  2. Summarize the study in one  paragraph (about 200-250 words)
  3. Research on the first three authors’ professional and academic qualifications and state if they have previously published on this topic. If not try to explain how they would fit on this topic.
  4. Are the research questions/problems researchable? Is the problem presented important enough to justify this study?
  5. Is the literature review focused on the issue? Does it reveal any gaps the authors wanted to fill?
  6. Is the aim of the study clear and shows what the study wants to achieve?
  7. Is there any framework or theory of DM used or applied in any way?
  8. Do you find the information/data used relevant to the aim/question of the study?
  9. Are the study results relevant to your group perspective? State your group in your response.
  10. Do you agree with the  conclusions made by the authors? 
  11. Did the authors put emphasis on the most important results of the study?
  12. What would you have changed in this study, why?
  13. In the conclusion, were there any new perspectives on the topic presented?


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  1. Prepare between 7 and 10 of double-spaced, typed pages in Word using 12-point NTR font.
  2. The paper carries 30% of the total grade.
  3. APA style should be used throughout.

Shared Decision Making and Other Variables as Correlates of
Satisfaction with Health Care Decisions in a United States
National Survey

Katherine Elizabeth Glass, MPH,
The Ohio State University, 1585 Neil Avenue, Columbus, OH 43210. Telephone: 614.292.4524.
FAX: 614.292.7976.

Celia E. Wills, Ph.D., R.N.,
Associate Professor & Sills Professor in Interdisciplinary Behavioral Health Nursing, The Ohio
State University College of Nursing, Columbus, Ohio U.S.A. wills.120@osu.edu

Christopher Holloman, Ph.D.,
Auxiliary Associate Professor, The Ohio State University Department of Statistics, Columbus,
Ohio U.S.A.

Jacklyn Olson,
Honors Baccalaureate Student in Nursing, The Ohio State University College of Nursing,
Columbus, Ohio U.S.A.

Catherine Hechmer, B.A.,
Master’s Student in Clinical Social Work, The Ohio State University College of Social Work,
Columbus, Ohio U.S.A.

Carla K. Miller, Ph.D., and
Associate Professor, The Ohio State University Department of Human Nutrition, Columbus, Ohio

Anne-Marie Duchemin, M.D.
Research Scientist, Associate Professor Adjunct, The Ohio State University Department of
Psychiatry, Columbus, Ohio U.S.A.

Objective—The purpose of this study was to examine the relationship between shared decision-
making (SDM) and satisfaction with decision (SWD) within a larger survey of patient decision-
making in health care consultations.

Methods—A randomly selected age-proportionate national sample of adults (aged 21–70 years)
stratified on race, ethnicity, and gender (N = 488) was recruited from a health research volunteer
registry and completed an online survey with reference to a recent health consultation. Measures
included the Shared Decision Making-9 questionnaire (SDM-Q-9), Satisfaction With Decision
(SWD) scale, sociodemographic, health, and other standardized decision-making measures.
Forward selection weighted multiple regression analysis was used to model correlates of SWD.

© 2012 Elsevier Ireland Ltd. All rights reserved.

Correspondence to: Katherine Elizabeth Glass.

Publisher’s Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our
customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of
the resulting proof before it is published in its final citable form. Please note that during the production process errors may be
discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

NIH Public Access
Author Manuscript

Patient Educ Couns. Author manuscript; available in PMC 2013 July 01.

Published in final edited form as:
Patient Educ Couns. 2012 July ; 88(1): 100–105. doi:10.1016/j.pec.2012.02.010.







Results—After controlling for sociodemographic variables, SDM-Q-9 total score was associated
with SWD, adjusted R2 = .368, p < .001. Three of nine SDM-Q-9 items accounted for significant proportions of variance in SWD.

Conclusion—SDM was positively associated with SWD and was strongest for three areas of
SDM: patients being helped in a health care consultation with understanding information, with
treatment preference elicitation, and with weighing options thoroughly.

Practice Implications—By identifying variables such as SDM that are associated with SWD,
health care interventions can better target modifiable factors to enhance satisfaction and other

shared decision-making; satisfaction with decision; patient-provider communication

1. Introduction
A majority of adults routinely engage in health-related decision-making that has significant
impact for personal health and well-being and broader public health outcomes. Recent
national surveys in North America document the high prevalence of patient decision-making
and associated unmet needs for decision-making support among patients who seek health
care services (1, 2). Shared decision-making (SDM) is receiving increasing attention as a
solution to better meet patient decision support needs by improving the quality of patient-
provider health care decision-making processes. SDM can be broadly defined as an
interactive, collaborative process between patients and health care providers that is used to
make health care decisions, that is characterized by several features of the patient-provider
interaction: (a) eliciting and acknowledging patients’ preferences for participation; (b)
giving choices as to how the decision-making process will proceed; and, (c) mutually
respecting and adhering to choices (3). SDM is advocated on premises that patients have a
right of self-determination, as well as an expectation that patient involvement in shared
decision-making can increase the likelihood of treatment adherence (4), in which adherence
is conceptualized as the extent to which a patient continues to implement a previously-made
treatment decision. Research on the relationship between SDM and adherence is important
because patients themselves usually control the extent to which they adhere to health
treatments, and adherence has significant impact on health and other outcomes.

Recently Stalmeier (2010) proposed a conceptual model to illustrate effects of decision aids
on adherence behavior (4). The model posits researchable hypotheses (described as
‘hypothetical’ pathways in the model) for how processes of communication and deliberation
in SDM can support treatment adherence. Specifically, decision aids are hypothesized to
strengthen attitudes toward choices (options), which in turn can strengthen the relationship
between attitudes and adherence behavior. Patient attitudes about options can be measured
in multiple ways, including but not limited to strength and persistence of treatment
preferences, and level of satisfaction with decisions. While there is as yet inconclusive
empirical support for the hypothesized linkages of SDM, attitudes, and adherence, prior
findings hold sufficient promise such that future studies are proposed to more definitely
examine these relationships (4, 5). From a public health perspective, information about the
association of shared decision-making and attitudes toward options is important because
these are potentially-modifiable variables that can inform improved interventions to support
treatment adherence, and improved adherence can lead to improved health outcomes.

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1.1. Study purpose
The purpose of this study was to examine the relationship of patient perceptions of shared
decision-making (SDM) and an attitude measure, satisfaction with decision (SWD) (6, 7),
within a larger survey that was conducted to validate a newly-developed measure of SDM,
the Shared Decision Making-9 questionnaire (SDM-Q-9), in a U.S. national sample (8, 9).
The secondary analysis reported in this paper extends findings of prior national surveys (1,
2) by examining the association of SDM and SWD for a recently-made decision related to
diagnosis, treatment, or referral for a personally-experienced health issue. The specific
analysis reported in this paper and recency of recall of decision-making (decision made in
the past 3 months) have not been addressed in prior national surveys which have used longer
recall time periods; i.e., ever having made a complex health-related decision (2), or having
made a medical decision within the past two years (1). Within the framework of the
Stalmeier model (4), the overall hypothesis examined in the present study was that SDM
would exhibit a positive relationship with SWD. Additional analyses were conducted to
further explore: (a) the contribution of specific aspects of SDM to SWD; and, (b) the
association of socio-demographic and other decision-making variables with SWD. To
provide context about the decision-making circumstances of the respondents and to place
our study results in context of other surveys of health-related decision-making (1, 2), we
also summarized the types of reported health-related decisions (relating to diagnosis,
treatment or referral for health concerns) and the specific categories of health concerns that
were a focus of decision-making. We also were able to compare the socio-demographic
characteristics of our obtained sample to the ResearchMatch population.

2. Methods
2.1. Recruitment and sampling

The present study was based on a secondary analysis of data from a larger survey study that
was conducted to validate the Shared Decision Making Questionnaire-9 (SDM-Q-9) (8) in a
U.S. national sample. Recruitment for the study was done via ResearchMatch, a national
health volunteer registry that was created by several academic institutions and supported by
the U.S. National Institutes of Health as part of the Clinical Translational Science Award
(CTSA) program. ResearchMatch has a large population of volunteers who have consented
to be contacted by researchers about health studies for which they may be eligible. Review
and approval for the study and all procedures was obtained from The Ohio State University
Behavioral and Social Sciences Institutional Review Board.

A stratified random sampling plan was created by state to reflect the gender,race, and ethnic
distribution of the 2000 U.S. population census data for persons between the ages of 21 and
70. States were clustered into one of six geographic regions. Each region had an associated
list of individuals aged 21–70 years to sample by state, within dichotomized strata of gender,
race, and ethnicity. If a given state did not have enough ResearchMatch volunteers to reflect
the sample needed to match the sampling plan, additional individuals were drawn from (a)
larger state(s) within the same region. For states with more volunteers than needed to be
sampled, a random number table was generated to select which individuals to contact. The
study invitation was sent via ResearchMatch to the selected volunteers. The study flow
diagram in Figure 1 shows the study recruitment, sampling, and survey processes.

2.2. Measures
The survey consisted of measures of respondent characteristics and measures related to the
process of decision-making. Decision-making questionnaires were completed with reference
to a specific consultation each respondent confirmed had occurred with a health care
provider within 3 months of participation in the survey. Respondents were asked to report

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age, race, gender, state of residence, ethnicity, employment status, health insurance status,
marital status, and education level. They were asked to specify the reason for their visit to a
health care provider in open text format, and to describe any diagnostic, treatment, or
referral decisions made about their care at that visit. The final portion of the survey
consisted of several widely-used, standardized self-report measures of decision-making: the
Decisional Conflict Scale (DCS) (10), Autonomy Preference Scale (APS) (11), Shared
Decision Making Questionnaire-9 (SDM-Q-9) (8), and Satisfaction with Decision (SWD)
scale (6, 7). The standard 16-item version of the DCS was used to assess uncertainty in
making a health-related decision, factors contributing to uncertainty, and perceptions of
effective decision-making. Each item is rated on a 1 (strongly agree) to 5 (strongly disagree)
scale. Higher scores indicate more decisional conflict. The Control (Autonomy) Preference
Scale (APS) is a single-item measure of the extent to which the respondent wishes to
exercise control in making a decision, rated on a 1 to 5 ordinal scale, with equally shared
decision-making rated as a 3. A score of 1 indicates a preference for full patient autonomy in
decision-making, while a score of 5 corresponds to preferring to have another make the
entire decision. The SDM-Q-9 is a 9-item self-report measure assessing patient perceptions
of the extent to which SDM occurred in a specific patient-provider consultation. The SDM-
Q-9 is newly- validated and has been shown to have a unidimensional factor structure and
high Cronbach alpha (α) internal consistency coefficients in German (.98) and U.S. (.94)
populations. The items are rated on a 0 to 5 scale, with 0 indicating ‘completely disagree’
and 5 indicating ‘completely agree’. Higher scores indicate higher perceived levels of SDM
during a patient-provider consultation. The SWD scale contains six items that assess patient
satisfaction with a health care decision, rated on a 1–5 scale (1 strongly disagree; 5 strongly
agree). Higher scores indicate higher satisfaction with decision. With the exception of the
single-item APS measure, a raw sum score was computed for each decision-making
measure, followed by transforming the raw sum scores to an in-common 0 to 100 scale for
use in analyses.

2.3. Content coding of decisions
The types of decisions and reasons for health care consultations were content-coded to be
able to compare this research to previous surveys (1, 2). Coding was performed for the two
open-ended questions for the specific decision being made by the respondent: (1) “Please
indicate which health complaint/problem/illness the consultation was about,” and (2) “Please
indicate which decision was made.” The lead researcher (K.G.), in collaboration with a
second researcher (C.W.), developed the initial categories into which the content could be
appropriately divided, and did the initial coding. If a respondent indicated several reasons
for visiting a health care provider, only those that were associated with a clear decision in
the consultation pertaining to diagnosis, treatment, or referral were coded. The initial
categories were discussed and refined with other research team members. Next, the third
researcher (J.O), using the categories devised by the primary researcher, performed a second
independent coding of the data. The results of the two separate coding efforts were then
compared for concordance between the three researchers, and the inter-rater reliability was
calculated to determine the initial and final raw percentage agreement among the two
primary coders (K.G., J.O.). The initial interrater raw percentage reliability was 83%.
Discussion of the areas of content coding where the researchers did not agree took place
with the third researcher (C.W.) who was not otherwise involved in the coding process and
aided in reaching decisions on the appropriate content code. Most coding discrepancies
involved a coder making an inference from the data that could not be fully supported based
on the available information. The subset of ‘not codable’ responses was also reviewed
within the larger research team to reconfirm the coding decisions that were made. All coding
discrepancies were able to be resolved via the discussion process, resulting in a final
interrater reliability of 100%.

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2.4. Scoring and analysis
Forward selection multiple linear regression analysis was used to model correlates of SWD.
IBM SPSS V19.0.0 was used to analyze the sample descriptive statistics. The regression
analysis and results for the weighted modeling were performed with the SURVEYREG
procedure in SAS 9.2. For the regression modeling, two sociodemographic measures, race
and education level, were converted into dichotomous categories. Race was condensed from
six original categories to “white” and “not white”, and education level (originally with 7
categories) was dichotomized as “less than high school education” and “high school
education or greater.” For the decision questionnaires (DCS, SDM-Q-9, and SWD), total
raw scores were linearly transformed to an in-common 0–100 scale. The APS scale was not
transformed to a 0–100 scale as reasonable interpretation of the raw score is possible. The
dependent variable, SWD sum total score, was treated as a continuous variable.

3. Results
3.1. Sample characteristics

Table 1 displays the overall sample characteristics for socio-demographic variables. Figure 1
presents the study flow diagram. The obtained sample had similarities and some differences
compared to the overall ResearchMatch population. The sample was somewhat higher on
proportion of white respondents, lower on African-American respondents, and higher on
Hispanic respondents compared to ResearchMatch (82%, 10%, 5%, respectively). The
obtained sample had similar percentages to ResearchMatch for Asian, American Indian/
Alaskan Native, and other racial groups. The sample was more evenly distributed on gender
(64.7% female, 35.3% male) compared to ResearchMatch (73.0% female, 27.0% male). The
only groups that fully met the target sample sizes were white and non-white non-Hispanic
females. There are larger sampling deficits for men of all races and ethnicities due to their
underrepresentation in ResearchMatch.

3.2. Content coding categories
The ‘reason for visit’ responses were placed into one of 15 reasons for consultations
categories (Table 2a), with a total 522 non-mutually-exclusive reasons for health
consultations reported. The most frequent reason for consultation was for a musculoskeletal
issue, followed by neurological and infection. The ‘type of decision’ responses were coded
as diagnosis, treatment, or referral (Table 2b). Of the 562 codable health care decisions
reported by survey respondents, the large majority were treatment decisions.

3.3. Descriptive statistics for standardized measures
Table 3 summarizes the total scores for the four standardized decision-making measures
included in the survey. The majority (65.6%) indicated that their autonomy preference was a
2 on the 1 to 5 rating scale, corresponding with the response option, “I should make the
decision on my own, but take others’ opinions into account.” The average SDM-Q-9 score
showed that the mean of the sample was above the midpoint of the scale in their perceptions
of the extent to which shared decision-making was present in their index health care
consultation. The SWD total score was relatively high, reflecting that the average
respondent was satisfied with rated health care decisions.

3.4. Regression modeling
Variables in addition to the SDM-Q-9 measure that were included in the initial linear
regression model were: dichotomized race, gender, ethnicity, education level, marital status,
and age, autonomy preference (APS), and standardized total scores for decisional conflict
(DCS). Non-modifiable respondent characteristics (race, gender, ethnicity, education level,

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marital status, age, and APS) were controlled for by grouping them together in a block. DCS
and SDM-Q-9 were conceptualized as potentially modifiable variables, and were added to
the model after controlling for the non-modifiable variables.

The linear regression model (Table 4) revealed that total SDM-Q-9 score was a statistically
significant predictor of SWD, p < 0.001. DCS total score approached statistical significance with a p-value of 0.09. While DCS displayed the expected negative relationship with SWD, i.e., higher decisional conflict was associated with lower decision satisfaction, the relatively small effect size was unexpected, and may be due to the negatively skewed distribution of DCS scores. Variance inflation factor (VIF) values for all variables in the model were at or around 1, suggesting little collinearity between them. The adjusted R2 value indicated that 36.8% of the variation in SWD was explained by the variables included in the model, and primarily by SDM-Q-9 score.

Because SDM-Q-9 total score was significantly associated with SWD, a second model was
run on an exploratory basis to ascertain which items of the SDM-Q-9 were more or less
strongly associated with SWD. This second model held all other variables and procedures
the same as the previous model, except for the substitution of each of the nine individual
item scores comprising the SDM-Q-9 questionnaire (Table 5). DCS was again marginally
significant (p = 0.09) and displayed the expected negative association with SWD (Table 5).
Of the 9 SDM-Q-9 items, 3 displayed significant, positive correlations: (a) Item 5 (“My
doctor helped me understand all the information”), (b) Item 6 (“My doctor asked me which
treatment I prefer”), and, (c) Item 7 (“My doctor and I thoroughly weighed the different
treatment options”). The SDM-Q-9 individual items were somewhat collinear with VIF
values ranging from 1.4 to 4.7, but not markedly increased over the initial model.
Collinearity of the remaining variables remained low. In this exploratory model, p-values
were examined only to quantify the potential strength of statistical significance and not as a
strict testing exercise. In a formal testing event, only Item 5 (p < .001) would remain statistically significant based on a Bonferroni adjustment.

4. Discussion and Conclusion
4.1. Discussion

As hypothesized, SDM-Q-9 score was positively associated with SWD. By identifying
factors such as SDM that may increase satisfaction with decisions, this can support
improved intervention approaches to encourage patients to be involved in their own care,
including collaboration on health management plans that they will be more likely to follow
if they are satisfied with their decisions. Although there are many factors that contribute to
whether or not patients are satisfied with their care, one aspect of satisfaction with a health
care experience is clearly the quality of interaction during decision-making with the health
care provider. If SDM can improve the quality of that interaction, the result of this study is
consistent with the premise that SWD may lead to improved adherence by increasing overall
satisfaction with care.

The regression model in which the individual items of the SDM-Q-9 were used instead of
the total score was done on an exploratory basis, to examine if some items were more
associated with SWD than others. While only three of the nine items on the SDM-Q-9 were
significantly correlated with SWD, it is of interest that the nine items have such a wide range
of association with SWD, including some that were not significantly associated with SWD.
The significant association of SWD with these three SDM-Q-9 items is consistent with prior
research, linking understanding of what matters to the patient for their overall satisfaction
with their health care encounter. However, the statistical modeling accomplished in this
study is a more explicit examination of the relationship of these individual aspects of SDM

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to SWD compared to previous research. The reported results allow a deeper look into what
patients perceive makes SDM a desirable approach in health care consultations. The
implication from the data is that some aspects of SDM may be more highly valued than
others. Interestingly, the highest correlation with SWD was with “doctor helped to
understand information.” The two other significant items: patient’s preference of treatment
option, and weighing different options together, had significant associations with SWD,
while “selecting a treatment option together” and “reaching an agreement” did not. This
seems to suggest that once patients understand the information, having the ability to choose
increases their SWD, while making decision together with the health provider does not.
Perhaps in the future, a shorter version of the SDM-Q-9 questionnaire might be developed
that retains strong psychometric properties and effectiveness in predicting health care
outcomes. More research regarding how each of the nine items relate to a broader range of
outcomes is recommended.

The analysis of the decision content coding demonstrates the diversity of the type of
decisions being made, which is consistent with prior North American national surveys (1, 2).
This study provides detail about specific elements of shared decision-making that were
perceived as present by examining the individual components of the SDM-Q-9 scale. In
addition, a more recent timeframe for decision-making was examined (past 3 months),
which is substantially briefer compared to prior national surveys.

There were several limitations to this study. Although not central to the purpose of the study,
the relationship between SDM and adherence was not assessed in the larger cross-sectional
survey study to validate the SDM-Q-9 scale in a U.S. population. A more definitive test of a
causal relationship between SDM and adherence would require a longitudinal experimental
design. Self-reported adherence for the timeframe of the study recall period (past 3 months)
could be challenging to analyze due to the wide variety of decisions and acute and chronic
health conditions contexts reported by respondents. Recall bias could have influenced
survey responses otherwise, as respondents were asked to retrospectively recall a health-
related decision. However, this window of time is relatively small, and is substantially
briefer compared to two previous national surveys (1, 2). Lastly, the sample was relatively
well-educated and 95% reported having at least some health insurance. These variables
could account in part for the relatively high overall level of satisfaction with health decisions
reported in this study and show that the sample was not fully representative of the total U.S.
population. Because ResearchMatch is an online database, the ability to participate and learn
about studies is limited to those who can access an Internet-equipped computer. There may
also be a selection bias of the population based on their interest in medical research that
could explain the relatively high percentage of SWD. ResearchMatch is being implemented
at a number of academic medical institutions nationwide, but some states and regions
contain many more volunteers than others. Nonetheless, the obtained sample represents
users of health care services for whom research on shared decision-making is relevant.
Results are similar to prior national surveys in terms of the extent to which respondents
reported being involved in decision-making, and in the wide range of decisions and health
care issues represented in their survey responses. Thus, although the study population may
not be representative of the U.S. as a whole, the results still provide valuable insights on the
relationship between SDM and SWD, and are relevant to populations that seek health care

4.2. Conclusion
The statistical modeling strategy identified factors correlated with SWD, especially SDM
and specific aspects of the SDM questionnaire. These findings are consistent with the
adapted model, in that we were able to document a hypothesized relationship between SDM
and SWD. More research on the effects of shared decision-making on patient satisfaction is

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encouraged, as is research on the relationship between shared decision-making and
adherence (via attitudes), for both chronic and acute conditions, and for a wide variety of
individuals and populations.

4.3. Practice implications
By identifying variables such as SDM that are significantly associated with satisfaction in a
health care decision, the public health and broader health care communities can better target
potentially modifiable variables to enhance decision satisfaction and clinical outcomes.

The authors thank Drs. Amy Ferketich and Randi Foraker of The Ohio State University College of Public Health
for their reviews of earlier drafts of this manuscript. The results reported in this manuscript were used in the lead
author’s Master of Public Health (MPH) Culminating Project, completed in partial fulfillment of requirements for
the MPH degree. Portions of the results were presented at the June 2011 International Shared Decision Making
Conference in Maastricht, The Netherlands. The project was supported by the resources of Award Number
UL1RR025755 to the Ohio State University Center for Clinical Translational Science from the U.S. National
Center for Research Resources. The content is solely the responsibility of the authors and does not necessarily
represent the official views of the U.S. National Center for Research Resources of the National Institutes of Health.

The authors confirm all patient/personal identifiers have been removed or disguised so the patient/person(s)
described are not identifiable and cannot be identified through the details of the study.

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study: a nationwide survey of United States adults regarding 9 common medical decisions. Med
Dec Making. 2010; 30:20S–34S.

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making needs of Canadians faced with complex health decisions. Health Expect. 2003; 6:97–109.
[PubMed: 12752738]

3. Charles C, Gafni A, Whelan T. Shared decision-making in the medical encounter: what does it
mean? (or it takes two to tango). Soc Sci Med. 1997; 44:681–92. [PubMed: 9032835]

4. Stalmeier PFM T. Adherence and decision aids: a model and a narrative review. Med Dec Making.
2010; 31:121–9.

5. Stacey D, Bennett CL, Barry MJ, Col NF, Eden KB, Holmes-Rovner M, et al. Decision aids for
people facing health treatment or screening decisions. Coch Database Syst Rev. 2011; (10)

6. Holmes-Rovner M, Kroll J, Schmitt N, Rovner DR, Breer ML, Rothert ML, et al. Patient
satisfaction with health care decisions: the Satisfaction With Decision Scale. Med Dec Making.
1996; 16:58–64.

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8. Kriston L, Scholl I, Holzel L, Simon D, Loh A, Harter M. The 9-item shared decision making
questionnaire (SDM-Q-9). Development and psychometric properties in a primary care sample. Pat
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9. Wills, CE.; Glass, KE.; Holloman, C.; Hechmer, C.; Olson, J.; Miller, C., et al. Validation of the
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Figure 1. Study Flow Diagram
Of 10,987 ResearchMatch volunteers listed as being between 21 and 70 years of age, 4,389
were randomly selected to receive the initial study invitation. If a volunteer replied “YES” to
the study invitation, the volunteer was sent a link to the online study survey. The survey
consent procedure asked the respondent to confirm that s/he met study eligibility criteria by:
(a) being between 21 and 70 years of age; and, (b) having had a consultation with a health
care provider within the past 3 months for diagnosis, treatment, or referral related to a
personally experienced health issue.

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Glass et al. Page 10

Table 1

Sociodemographic Characteristics (n=488)

Characteristic N %

Age (in years)

Mean 41.4; SD 13.3

Race 482

White 410 85.1

Black/African Descent 31 6.4

Asian 20 4.1

Other 18 3.7

American Indian/Alaskan Native 3 0.6

Ethnicity 479

Non-Hispanic 439 91.6

Hispanic 40 8.4

Gender 485

Female 314 64.7

Male 171 35.3

Employment Status 481

Employed 363 75.5

Unemployed 118 24.5

Marital Status 483

Married 300 62.0

Not Married 183 38.0

Education Level 485

Graduate/Professional Degree 182 37.5

College (completed degree) 132 27.2

Some Graduate/Professional 80 16.5

Some College/Technical Training 73 15.1

High School (12th grade) 17 3.5

Some High School (<12th grade) 1 0.2

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Table 2

a Content coding for the question, “Please indicate which health
complaint/problem/illness the consultation was about”

Reason for Visit N %

Musculoskeletal 91 18.6

Neurological 70 14.3

Infection 64 13.1

Cardiovascular 55 11.2

Psychological 51 10.4

Gastrointestinal 38 7.8

Endocrinological 34 7.0

Dermatology 33 6.8

Reproductive 22 4.5

Pulmonary 15 3.1

Cancer 13 2.7

Weight Issue 12 2.5

Allergies 11 2.3

Urinary 10 2.0

Prevention 3 < 1.0

b Content coding for the question, “Please
indicate the decision made”

Type of Decision N %

Treatment 402 82.4

Diagnosis 122 25

Referral 38 7.8

Ns are > 488 due to some participants reporting multiple reasons for consultations; percentage denominator is 522

Ns are > 488 due to some participants reporting multiple decisions; percentage denominator is 562

Patient Educ Couns. Author manuscript; available in PMC 2013 July 01.


Glass et al. Page 12

Table 3

Descriptive Statistics for Decision-making Measures (n=488)

Measure N Mean SD

Autonomy Preference (APS)a 462 2.3 .62

Decisional Conflict Scale (DCS) Total Scoreb 472 37.1 31.0

Shared Decision Making (SDM-Q-9) Total Scorec 479 67.6 26.6

Satisfaction with Decision (SWD) Total Scored 473 76.7 19.6

1 – 5 ordinal scale:1 = preference for complete autonomy, 3 = preference for equally shared decision; 5 = preference for others to make the


0 – 100 scale; higher scores indicate higher decisional conflict

0 – 100 scale; higher scores indicate higher perceived shared decision-making

0 – 100 scale; higher scores indicate higher decision satisfaction

Patient Educ Couns. Author manuscript; available in PMC 2013 July 01.


Glass et al. Page 13

Table 4

Multiple linear regression (dependent variable = SWD)

Variable β SE(β) t Sig.

(constant) 49.520 6.151 8.05 <.001

Gender 0.427 1.474 0.29 0.772

Race (White/Non-White) 0.598 2.199 0.27 0.786

Employment (Employed/Not Employed) 1.061 1.855 0.57 0.568

Education Level (Less than High School/High School or greater) 2.13 1.886 1.13 0.259

Marital Status ( Married/Not Married) 0.244 1.671 0.15 0.884

Ethnicity (Hispanic/non-Hispanic) −1.517 2.775 −0.55 0.585

Age (in years) 0.032 0.062 0.51 0.612

Autonomy Preference (APS) −1.60 1.311 −1.22 0.223

Decisional Conflict Scale (DCS) Total Score −0.042 0.025 −1.71 0.089

Shared Decision Making (SDM-Q–9) Total Score 0.429 0.034 12.60 <.001

Note: R2= .368

Patient Educ Couns. Author manuscript; available in PMC 2013 July 01.


Glass et al. Page 14

Table 5

Multiple linear regression with individual SDM-Q-9 items (dependent variable = SWD)

Variable β SE(β) t Sig.

(constant) 50.551 6.399 7.90 <.001

SDM-Q-1: My doctor made clear that a decision needs to
be made

0.177 0.652 0.27 0.787

SDM-Q-2: My doctor wanted to know exactly how I
wanted to be involved in making the decision

−0.884 0.822 −1.08 0.283

SDMQ-3: My doctor told me that there are different
options for treating my medical condition

−1.049 0.665 −1.58 0.115

SDMQ-4: My doctor precisely explained the advantages
and disadvantages of the treatment options

−0.028 0.886 −0.03 0.975

SDMQ-5: My doctor helped me understand all of the

3.554 1.075 3.31 0.001

SDMQ-6: My doctor asked me what treatment option I

2.063 0.982 2.10 0.036

SDMQ-7: My doctor and I thoroughly weighed the
different treatment options

2.435 1.186 2.05 0.041

SDMQ-8: My doctor and I selected a treatment option

0.514 0.956 0.54 0.591

SDMQ-9: My doctor and I reached an agreement on how
to proceed

1.756 1.075 1.63 0.103

Note: R2= .410

Patient Educ Couns. Author manuscript; available in PMC 2013 July 01.

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