Influence of remedial professional development programs for poorly performing physicians
Bibliographic record
Abstract
INTRODUCTION: The Collège des Médecins du Québec (CMQ) offers an individualized remedial professional development program to help physicians overcome selected clinical shortcomings. To measure the influence of the remedial professional development program, physicians who completed the program between 1993 and 2004 and who were assessed by peer review during a 2-year period preceding or following the remedial activities were tracked. METHODS: For each physician, 30 to 50 patient records were selected randomly for review. Ratings were assigned for the quality of record keeping and for 3 elements pertaining to the quality of care: the clinical investigation plan, diagnostic accuracy, and patient treatment and follow-up. The impact of the program was measured by comparing the proportion of physicians with satisfactory ratings assigned by peer review before and after the remedial professional development program. RESULTS: Statistically significant improvements (p < .05) were observed for a proportion of physicians (n = 51) with satisfactory ratings with regard to record keeping (20% before and 54% after remediation), the clinical investigation plan (13% before and 59% after remediation), diagnostic accuracy (32% before and 61% after remediation), and patient treatment and follow-up (31% before and 67% after remediation). DISCUSSION: Participation in a CMQ remedial professional development program can result in improved clinical performance, as assessed through peer review.
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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 | 0.005 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".