Développement et mise à l’essai du Guide de rétroaction francophone pour l’observation directe des résidents en médecine familiale au Canada
Bibliographic record
Abstract
Background: There is no CanMEDS-FM-based milestone tool to guide feedback during direct observation (DO). We have developed a guide to support documentation of feedback for direct observation (DO) in Canadian family medicine (FM) programs. Methods: The Guide was designed in three phases with the collaboration of five Canadian FM programs with at least a French-speaking teaching site: 1) literature review and needs assessment; 2) development of the DO Feedback Guide; 3) testing the Guide in a video simulation context with qualitative content analysis. Results: Phase 1 demonstrated the need for a narrative guide aimed at 1) specifying mutual expectations according to the resident's level of training and the clinical context, 2) providing the supervisor with tools and structure in his observations 3) to facilitate documentation of feedback. Phase 2 made it possible to develop the Guide, in paper and electronic formats, meeting the needs identified. In phase 3, 15 supervisors used the guide for three levels of residence. The Guide was adjusted following this testing to recall the phases of the clinical encounter that were often forgotten during feedback (before consultation, diagnosis and follow-up), and to suggest types of formulation to be favored (stimulating questions, questions of clarification, reflections). Conclusion: Based on evidence and a collaborative approach, this Guide will equip French-speaking Canadian supervisors and residents performing DO in family medicine.
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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 | 0.034 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".