Academic Half-Days: Facilitated Small Groups to Promote Interactive Learning
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Medical educators have expressed interest in using less didactic and more interactive formats for academic half-days (AHDs) in postgraduate residency training. We assessed the feasibility and effectiveness of implementing a practice-based small-group learning (PBSGL) process as one part of AHDs. METHODS: A mixed-methods approach was used. Over a two-year period, family medicine residents at the University of Calgary took part in PBSGL sessions during their AHDs, discussing clinical cases presented in evidence-based educational modules and reflecting on clinical experiences with the guidance of a trained peer facilitator. Data sources to explore experiences with the PBSGL process included an evaluation questionnaire, a practice reflection tool (PRT; documenting patient management plans) and individual interviews (n=19) with residents and faculty preceptors. RESULTS: Of 148 residents, 139 (93%) agreed to participate. Participants were divided into groups of 14-16 members to discuss 12 different module topics. Participants indicated that ongoing small-group interactions were helpful in meeting learning needs and provided opportunities to share and learn from experiences of others in a safe environment. Group facilitation by residents was successful. Level of resident participation and time to preread modules were factors contributing to successful small-group interactions. Modules were rated as effective learning tools, and sample cases were perceived as representing typical cases encountered in practice. Although participants intended to apply their learning to practice, follow through was hindered by lack of relevant clinical cases. CONCLUSIONS: Ongoing small-group learning facilitated by residents, coupled with evidence-based educational materials, was a feasible approach to AHDs.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".