La création et l’implantation réussie d’un outil de remédiation en résidence de médecine familiale
Bibliographic record
Abstract
PROBLEM BEING ADDRESSED: As is true in most postgraduate medical education programs, about 10% of the residents in the family medicine residency program at Université de Montréal encounter considerable difficulties in developing their skills. OBJECTIVE OF PROGRAM: In order to more adequately support the program’s teachers in diagnosing these difficulties and in designing, planning, and following up on a remediation strategy, the Residency Program Evaluation Committee devised a tool consisting of a sample remediation plan and a guide to its use. PROGRAM DESCRIPTION: The remediation tool consists of 2 documents. The first is a sample remediation plan made up of a contract followed by 4 sections: diagnosis of learning problems, intention to improve, ways to improve, and evaluation of improvement with an interim and a final report. The second is a guide to drafting and systematizing the remediation plan. CONCLUSION: The favourable response to the tool and the use that was made of it during the year in which it was implemented demonstrate that the processes we had chosen were a success. Support from the faculty, implementation of the co-construction method to create the tool, as well as training and support for users were all factors in this success. A research project is under way to document the impact that use of this tool will have on our residency program.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".