Predicting doctor performance outcomes of curriculum interventions: problem‐based learning and continuing competence
Bibliographic record
Abstract
CONTEXT: Problem-based learning (PBL) is an educational strategy designed to enhance self-assessment, self-directed learning and lifelong learning. The present study examines a peer review programme to determine whether the impact of PBL on continuing competence can be detected in practice. OBJECTIVES: This study aimed to establish whether McMaster graduates who graduated between 1972 and 1991 were any less likely to be identified as having issues of competence by a systematic peer review programme than graduates of other Ontario medical schools. METHODS: We identified a total of 1166 doctors who had graduated after 1972 and had completed a mandated peer review programme. Of these, 108 had graduated from McMaster and 857 from other Canadian schools. School of graduation was cross-tabulated against peer rating. A secondary analysis examined predictors of ratings using multiple regression. RESULTS: We found that 4% of McMaster graduates and 5% of other graduates were deemed to demonstrate cause for concern or serious concern, and that 24% of McMaster doctors and 28% of other doctors were rated as excellent. These differences were not significant. Multiple regression indicated that certification by family medicine or a specialty, female gender and younger age were all predictors of practice outcomes, but school of graduation was not. CONCLUSIONS: There is no evidence from this study that PBL graduates are better able to maintain competence than graduates of conventional schools. The study highlights potential problems in attempting to link undergraduate educational interventions to doctor performance 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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".