Evaluation of Residency Programs: A Novel Approach Using Simulation
Bibliographic record
Abstract
BACKGROUND: In Canada, there has been discussion about restructuring the accreditation process for residency education programs with the possibility of exempting selected programs from regular, on-site, external reviews. OBJECTIVE: We assessed the feasibility and acceptability of a structured and rigorous internal review that identified program strengths and weaknesses, with the aim of allowing well-performing programs to be exempt from external reviews or to facilitate a significant lengthening of the review cycle. METHODS: We simulated all aspects of a regular, on-site, external review. All participants (program directors, program coordinators, faculty surveyors, and resident surveyors) were trained and performed all components of a formal external review. Participants completed an online survey to assess perceptions of the process and outcome. RESULTS: The overall response rate was 73% (109 of 149). Most respondents perceived the process to be either extremely or very rigorous (84%), fair (82%), and unbiased (75%). Those with previous review experience (77%) reported that the internal review process simulated a regular, on-site, external review either well or very well (mean rating 4.87, SD 0.90). Most program directors reported the cited list of program strengths to be either extremely or very appropriate (74%, 26 of 35). Perceptions of fairness, bias, and the appropriateness of cited program strengths and weaknesses depended on review outcome. CONCLUSIONS: A structured and rigorous internal review process that simulates a regular, on-site, external review is feasible and could yield a list of program strengths and weaknesses for use in ongoing assessment and improvement.
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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 | 0.019 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".