Value of unstructured time (breaks) during formal continuing medical education events
Bibliographic record
Abstract
BACKGROUND: Unstructured time (breaks) at formal continuing medical education (CME) events is nonaccredited in some jurisdictions. Program participants, however, perceive this time as valuable to their learning. The purpose of this research was to determine what activities occur during unstructured time in formal CME events and how these activities impact learning for physicians. METHODS: A qualitative method based on grounded theory was used to determine themes of behavior. Both individual and focus group interviews were conducted. Data were analyzed and coded into themes, which were then further explored and validated by the use of a questionnaire survey. RESULTS: One hundred ninety-seven family physicians were involved in the study. Several activities related to the enhancement of learning were identified and grouped into themes. There were few differences in the ranking of importance between the themes identified, nor were differences determined based on gender or type of CME in which the break occurred. FINDINGS: The results suggest that unstructured time (breaks) should be included in formal CME events to help physician learners integrate new material, solve individual practice problems, and make new meaning out of their experience. The interaction between colleagues that occurs as a result of the provision of breaks is perceived as crucial in aiding the process of applying knowledge to practice.
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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 | 0.004 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".