Shared Decision-Making Training in Family Medicine Residency: A Scoping Review
Bibliographic record
Abstract
Shared decisions, in which physicians and patients share their agendas and make clinical decisions together, are optimal for patient-centered care. Shared decision-making (SDM) training in family medicine residency is always provided, but the best training approach for improving clinical practice is unclear. This review aims to identify the scope of the literature on SDM training in family medicine residency to better understand the opportunities for training in this area. Four databases (Embase, MEDLINE, Scopus, and Web of Science) were searched from their inception to November 2022. The search was limited to English language and text words for the following four components: (1) family medicine, (2) residency, (3) SDM, and (4) training. Of the 522 unique articles, six studies were included for data extraction and synthesis. Four studies referenced three training programs that included SDM and disease- or condition-specific issues. These programs showed positive effects on family medicine residents' knowledge, skills, and willingness to engage in SDM. Two studies outlined the requirements for SDM training in postgraduate medical education at the national level, and detailed the educational needs of family medicine residents. Purposeful SDM training during family medicine residency improves residents' knowledge, skills, and willingness to engage in SDM. Future studies should explore the effects of SDM training on clinical practice and patient care.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".