Small-group format for continuing medical education: A report from the field
Bibliographic record
Abstract
BACKGROUND: For continuing medical education (CME) to be effective, several key features must be realized. These include a learner-directed agenda of topics, presentation of information by trusted peers or local experts, and opportunity for practice and feedback. If the information comes from several sources--printed materials, peer discussion, patient questions, and presentation from the specialist community--the perception of need for and the durability of change are enhanced. Finally, motivation for change must be high enough for change to occur, yet not overwhelming. METHOD: Facilitated small-group discussion among general practitioner colleagues with an expert specialist around clinic-based problems meets many of these requirements. When followed up by relevant literature, key concepts and practice changes are reinforced. RESULTS: We discuss our 3-year experience with the small-group format, comprising more than 25 sessions as either learners or facilitators. We describe the maturation of our group. We highlight the benefits to learners, including the relevance to clinical practice and the opportunity to ascertain the standard of care of peers. The benefits to the specialist are also discussed, including opportunities to learn which suggestions are difficult to implement. IMPLICATIONS: Our experience demonstrates that this format is sustainable over the long term. The success of the small-group format at improving CME and patient outcomes deserves further evaluation.
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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 | 0.037 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".