Results of Remedial Continuing Medical Education in Dyscompetent Physicians
Bibliographic record
Abstract
PURPOSE: Noticing that moderately to severely incompetent physicians (as measured by a standardized assessment of physician competence) did not improve after traditional remedial continuing medical education (CME), the authors investigated the effects of a polyvalent, intensive, prolonged educational intervention on five physicians' competence. METHOD: The five physicians participated in a CME program that lasted three years and consisted of individualized review, ongoing small-group and evidence-based discussions, simulated patients and role playing, formal chart review, and peer review. At the end of the program, the physicians were reassessed. RESULTS: Only one physician improved; another remained the same, and three deteriorated. CONCLUSION: Successful remediation of severely incompetent physicians is uncertain at best, even with prolonged, intensive CME that incorporates modalities thought to be effective in changing physicians' behaviors. Alternative educational techniques may need to be developed for this select population. Conversely, there may be reasons that preclude improvement even with optimal techniques.
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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 | 0.015 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".