An Innovative Approach to Remedial Continuing Medical Education, 1992???2002
Bibliographic record
Abstract
The authors describe the process of remedial retraining programs organized and planned for Quebec physicians by the College des medecins du Quebec (CMQ) and report the outcomes of these efforts from April 1992 to March 2002. The CMQ (the Quebec medical licensing authority) developed a process to identify physicians who had shortcomings in their clinical performance, determine their educational needs, propose, in collaboration with the four medical schools in the province, personalized retraining programs (clinical training programs, tutorials, focused readings, workshops, and refresher courses), and subsequently evaluate the impact of these retraining programs. During the ten-year period reported, 305 physicians (216 family physicians and 89 specialists) were referred to the Practice Enhancement Division of the CMQ for personalized remedial retraining. The vast majority of these physicians were men (81%). The following difficulties were identified: therapeutic knowledge (37%), diagnostic knowledge (32%), record-keeping (14%), technical skills (10%), clinical judgment (5%), and communication skills (2%). A total of 329 personalized retraining programs were completed: 273 clinical training programs, 41 tutorials, and 15 focused readings. A reevaluation of all these physicians showed that 70% of the retraining programs had succeeded, 15% were partially successful and only 13% had failed. The remaining 2% involved missing data or withdrawal of physicians. The authors conclude that the collaborative CME process described has important and effective original features.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".