Some but not all dyadic measures in shared decision making research have satisfactory psychometric properties
Bibliographic record
Abstract
OBJECTIVE: To assess the psychometric properties of dyadic measures for shared decision making (SDM) research. STUDY DESIGN AND SETTING: We 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). RESULTS: We 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. CONCLUSION: The 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.093 | 0.427 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.008 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".