Some but not all dyadic measures in shared decision making research have satisfactory psychometric properties

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ObjectiveTo assess the psychometric properties of dyadic measures for shared decision making (SDM) research. Study Design and SettingWe conducted an observational cross-sectional study in 17 primary care clinics with physician-patient dyads. We used seven subscales to measure six elements of SDM: (1) defining the problem, presenting options, and discussing pros and cons; (2) clarifying the patient's values and preferences; (3) discussing the patient's self-efficacy; (4) drawing on the doctor's knowledge; (5) verifying the patient's understanding; and (6) assessing the patient's uncertainty. We assessed the reliability and invariance of the factorial structure and considered a measure to be dyadic if the factorial structure of the patient version was similar to that of the physician version and if there was equality of loading (no significant chi-square). ResultsWe analyzed data for 264 physicians and 269 patients. All measures except one showed adequate reliability (Cronbach alpha, 0.70–0.93) and factorial validity (root mean square error of approximation, 0.000–0.06). However, we found only four measures to be dyadic (P>0.05): the values clarification subscale, perceived behavioral subscale, information-verifying subscale, and uncertainty subscale. ConclusionThe subscales for values clarification, perceived behavioral control, information verifying, and uncertainty are appropriate dyadic measures for SDM research and can be used to derive dyadic indices.