Family medicine residents’ perspectives on shared decision-making: A mixed methods study
Bibliographic record
Abstract
OBJECTIVES: To 1) examine the willingness of residents to undertake shared decision-making and 2) explore whether the willingness to engage in shared decision-making is influenced by the perceived stakes of a clinical situation. METHODS: Sequential mixed methods design. Phase One: Family Medicine residents completed IncorpoRATE, a seven-item measure of clinician willingness to engage in shared decision making. Mean IncorpoRATE scores were calculated. Phase Two: We interviewed residents from phase one to explore their perceptions of high versus low stakes situations. Transcripts were analyzed using qualitative content analysis. RESULTS: IncorpoRATE scores indicated a greater willingness to engage in shared decision-making when the stakes of the decision were perceived as low (7.59 [2.0]) compared to high (4.38 [2.5]). Interviews revealed that residents held variable views of the stakes of similar clinical decisions. CONCLUSION: Residents are more willing to engage in shared decision-making when the stakes of the situation are perceived to be low. However, the interpretation of the stakes of clinical situations varies. PRACTICAL IMPLICATIONS: Further research is needed to explore how shared decision making is understood by residents in Family Medicine and when they view the process of shared decision-making to be most appropriate.
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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 | 0.038 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".