Educational skills and knowledge needed and problems encountered by continuing medical education providers
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to identify the training needs and difficulties encountered by continuing medical education (CME) providers in Quebec. METHODS: A questionnaire comprised of open-ended and closed questions was sent to 224 general practitioners across Quebec who organize CME meetings. To complement and validate the data, interviews were conducted with 18 physicians selected from this group, based on their years of experience with CME, and with the managers of two organizations involved in CME. RESULTS: The questionnaire response rate was 54%. Quantitative analysis was used to identify the main training needs expressed by CME providers affiliated with the Quebec Federation of General Practitioners, namely, methods for identifying needs (74%), group leadership techniques (69%), basic principles in adult education (69%), and organization of CME activities (66%). The main problems encountered by respondents in their duties are stimulating and maintaining the interest and participation of physicians in formal CME activities (52%), identifying and meeting physicians' educational needs (32%), and motivating physicians to get involved in any kind of CME initiative (18%). The interviews highlighted the wide disparity in the approaches used by CME providers when planning activities and the failure of providers to pass on relevant information to their successors. IMPLICATIONS: Based on the difficulties and the training needs identified, we were able to develop tools (structured training program, biannual newsletter, reference books, and resources) suited to the needs of general practitioners who organize CME activities.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".