Using incorpoRATE to examine clinician willingness to engage in shared decision making: A study of Family Medicine residents
Bibliographic record
Abstract
OBJECTIVES: We evaluated the willingness of Family Medicine residents to engage in SDM, before and after an educational intervention. METHODS: We delivered a lecture and a workshop for residents on implementing SDM in preventive health care. Before the lecture (T1), participants completed a measure of their willingness to engage in SDM. Six months later, participants completed the measure a second time (T2). RESULTS: At T1, 64 of 73 residents who attended the educational session completed incorpoRATE. Six months later, 44 of 64 participants completed the measure a second time (T2). The range of incorpoRATE sum scores at T1 was from 4.9 to 9.1 out of 10. Among the 44 participants who completed incorpoRATE at both time points, the mean scores were 7.0 ± 1.0 at T1 and 7.4 ± 1.0 at T2 (t = -2.833, p = 0.007, Cohen's D = 0.43). CONCLUSION: Among Family Medicine residents, the willingness to engage in SDM is highly variable. This suggests a lack of consensus in the mind of these residents about SDM. Although mean scores at T2 were significantly higher, we question the educational importance of this change. PRACTICE IMPLICATIONS: incorpoRATE is a promising measure for educators. Understanding how willing a particular physician audience is to undertake SDM, and which elements require attention, could be helpful in designing more targeted curricula. Further research is needed to understand how the perceived stakes of a clinical situation influence physician willingness to engage in SDM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".