Original ArticleThe actor–partner interdependence model in shared decision-making: an illustrative example of its application to the physician–patient dyad in primary care consultations
Introduction
The delivery of high-quality health care that adds value is a major goal for health organizations. Effective physician–patient communication is an essential step in the delivery of high-quality care [1]. Some studies have demonstrated that patients' and physicians’ perceptions of the communication process is not always concordant [2], [3]. Disagreements may even lead to litigation.
Shared decision-making is an interdependent process in which both “actors” have to share information and consider the patient's preferences to arrive at a common understanding of what constitutes the best medical decision for the patient [4], [5]. Communication processes can be modified to improve agreement between the two parties. Shared decision-making training thus involves educating physicians in communication skills and is often supported by the distribution of decision support materials [6].
Until recently, most research studied patients and physicians separately, using independent measures and disregarding their mutual influence [7]. However, as they are members of the same dyad and share a similar context or experience, their scores are likely to correlate. For example, uncertainty scores could correlate between patient and physician and indeed might correlate more with each succeeding visit as their relationship develops. Analysis of this correlation can capture complex communication processes more accurately, and data analysis methods that model interdependency are important when there is reason to expect dependencies in the data.
Types of perceptions in relationships have been described as self-perception, other perception, and meta-perception [8]. Self-perception represents how the individual views himself or herself [9]. Other perception represents how the individual views another person [10]. Meta-perception represents the perception that the person has of the other person's perception of himself or herself [11]. These types of perception may play a major role in measuring the interdependence process between two related persons.
Researchers in a number of domains use the actor–partner interdependence model [12], [13], [14] to analyze dyadic data [15]. It is especially useful in situations where variables vary both within and between dyads [16]. The actor–partner interdependence model simultaneously estimates the effects of one dyad member's characteristics and the other dyad member's characteristics on an outcome variable. The actor–partner interdependence model has been used in studies on interactions between husbands and wives [17], [18], parents and children [19], and romantic partners [20]. However, the method is not yet widely applied in health care to evaluate the relationship process between physician and his or her patient during a clinical encounter. The objective of this study was to provide an illustrative example of applying the actor–partner interdependence model to the EXACKTE2 (Exploiting the Clinical Consultation as a Knowledge Transfer and Exchange Environment) physician/patient data.
Section snippets
Study design and population
EXACTKE2 was a cross-sectional study conducted in 17 primary care clinics in two Canadian cities. EXACKTE2 used similar questionnaires for physicians and patients to produce dyadic data on the essential elements of shared decision-making [21]. A research assistant first recruited physicians and then patients during each physician's appointment hours in the waiting room at a randomly predetermined time. Eligible patients were at least aged 18 years, able to read French or English, willing to
Participants’ characteristics
A total of 263 unique dyads were recruited in primary care settings (Fig. 2). The mean age of physicians was 37 ± 11 years, and most were female (63%); the mean age of patients was 49 ± 11 years, and most were female (69%; Table 2). Most shared decision-making behavior variables had less than 2% of missing data, except for “physician's knowledge about the patient's health problem” for which there were 7.6% and 8.4% missing data for the physician and the patient, respectively (Table 3). There
Discussion
Our illustrative example sought to use the actor–partner interdependence model to measure whether patients' and physicians' perceptions of the physician's shared decision-making behaviors influence their own and/or the other's uncertainty about the decision. Our findings suggested that (1) the good adjustment of the actor-only pattern model showed that the perception of each member of the dyad about the physician's shared decision-making behaviors influenced only their own personal uncertainty
Conclusion
The results of our illustrative example could mean that each actor's perception of the physicians' shared decision-making behaviors affected their own uncertainty, but not the uncertainty of the other. However, the difference in the nature of dyad members' respective perceptions (other perception vs. self-perception) of the same behavior may have distorted results. Parallel questions for dyad members in which self-perceptions and other perceptions matched, or else questions generating
Acknowledgments
This study was funded by the Canadian Institutes of Health Research, Canada (CIHR 2008-2011; grant #185649-KTE). F.L. is Tier 1 Canada Research Chair in Shared Decision Making and Knowledge Translation.
References (32)
- et al.
An integrative model of shared decision making in medical encounters
Patient Educ Couns
(2006) Core reporting practices in structural equation modeling
Res Social Adm Pharm
(2008)- et al.
Some but not all dyadic measures in shared decision making research have satisfactory psychometric properties
J Clin Epidemiol
(2012) - et al.
Assessing competence in communication and interpersonal skills: the Kalamazoo II report
Acad Med
(2004) The role of the physician in the emerging health care environment
West J Med
(1998)- et al.
Doctor-patient communication and satisfaction with care in oncology
Curr Opin Oncol
(2005) - et al.
Framework for teaching and learning informed shared decision making
BMJ
(1999) Informed and shared decision-making: the crux of patient-centered care
CMAJ
(2001)- et al.
Interventions for improving the adoption of shared decision making by healthcare professionals
Cochrane Database Syst Rev
(2014) - et al.
Translating shared decision-making into health care clinical practices: proof of concepts
Implement Sci
(2008)
Interpersonal perception: a social relations analysis
The self-report method
Friend or foe? Differential use of the self-based heuristic as a function of relationship satisfaction
J Pers
Do people know how others view them? An empirical and theoretical account
Psychol Bull
The analysis of data from dyads and groups
Models of non-independence in dyadic research
J Soc Pers Relat
Cited by (6)
Perspectives From Adolescent and Young Adult Cancer Survivors for a Planned Nurse–Patient Dyadic Storytelling Intervention
2024, Journal of Holistic NursingPatient–dental student provider communication in an academic dental clinic setting: A dyadic data analysis
2023, Journal of Dental EducationRelationship Between Dyadic Coping with Anxiety and Depression in Infertile Couples: Gender Differences and Dyadic Interaction
2023, Psychology Research and Behavior ManagementNew resilience instrument for family caregivers in cancer: a multidimensional item response theory analysis
2021, Health and Quality of Life OutcomesUsing DEMATEL Technique to Identify the Key Success Factors of Shared Decision-Making Based on Influential Network Relationship Perspective
2021, Journal of Healthcare Engineering
Ethical considerations: Participants provided informed consent. Institutional Review Board approval for the study was obtained from the Research Ethics Board of the Centre de Santé et de Services Sociaux de la Vieille Capitale in Quebec City, Canada (final approval 2008/11/25; ethics number #2008-2009-23). Physicians and patients were not compensated for their participation.
Conflict of interest statement: The authors declare that they have no conflicts of interests.
Authors’ contributions: F.L., S.T., and H.R. participated in the concept and design of the study. F.L. and H.R. participated in data acquisition. S.T. performed the statistical analysis. All authors were involved in interpretation of the data and critical revision for important intellectual content of the article. S.T. and L.B. drafted the article. F.L. and H.R. coordinated the study. All authors read and approved the final article.