Remediation in Canadian medical residency programs: Established and emerging best practices
Bibliographic record
Abstract
Background: Policies to guide remediation in postgraduate medical education exist in all Canadian medical schools. This study examines concordance between these policies and processes, and published “best practices” in remediation. Method: We conducted a literature review to identify best practices in the area of remediation. We then reviewed remediation policies from all 13 English medical schools in Canada other than our own and conducted interviews with key informants from each institution. Each policy and interview transcript pair was then reviewed for evidence of pre-defined “best practices.” Team members also noted additional potential policy or process enablers of successful remediation. Results: Most policies and processes aligned with some but not all published best practices. For instance, all participating schools tailored remediation strategies to individual resident needs, and a majority encouraged faculty-student relationships during remediation. Conversely, few required the teaching of goal-setting, strategic planning, self-monitoring, and self-awareness. In addition, we identified avoidance of automatic training extension and the use of an educational review board to support the remediation process as enablers for success. Discussion: Remediation policies and practices in Canada align well with published best practices in this area. Based on key informant opinions, flexibility to avoid training extension and use of an educational review board may also support optimal remediation outcomes.
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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 | 0.036 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".