Family physicians’ continuing professional development activities: current practices and potential for new options
Bibliographic record
Abstract
BACKGROUND: As part of needs assessment processes, our Faculty of Medicine (FOM) continuing professional development office investigated the differences between physicians who do and those who do not frequently participate in planned group learning to gain insight into their interest in new forms of continuing professional development (CPD). METHOD: We sent a 19 item questionnaire to 485 randomly selected physicians of the 1050 family physicians in Eastern Ontario. The questionnaire examined present participation and satisfaction with CPD activities and perceptions regarding the potential impact of those; and appetite for new opportunities to meet their learning needs. RESULTS: Of the 151 (31%) physicians responding, 61% reported attending at least one FOM group learning program in the past 18 months (attenders) and 39% had not (non-attenders). Non-attenders indicated less satisfaction (p = 0.04) with present opportunities and requested development in newer approaches such as support for self-learning, on-line opportunities, and simulation. CONCLUSIONS: Although there are high levels of satisfaction with the present CPD system that predominantly offers large group learning options, a substantial number of physicians expressed interest in accessing new options such as personal study and on-line resources.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".