Teaching Surgeons How to Lead Interactive Workshops: A Needs Assessment
Bibliographic record
Abstract
BACKGROUND: Professional conferences traditionally use lectures and discussions for educating attendees; however, workshops incorporating small group instruction (SGI) provide more interactive learning. Many facilitators lack formal training in interactive pedagogy required for effective SGI. Understanding their preparation and delivery challenges is key to optimizing participant learning. STUDY DESIGN: This study evaluated a national surgical society's first implementation of SGI workshops at their annual conference. Healthcare professionals proposed, designed, and implemented their own curricula for other attendees. Facilitators received a handout outlining SGI best practices. After the conference, an electronic survey assessed facilitators' previous SGI experience, preparation, and challenges encountered. Free responses were categorized and analyzed descriptively. RESULTS: Of 163 facilitators of 44 workshops, 55 (33.7%) responded. Although >80% were highly familiar with their topic, most (67.3%) had little or no experience leading SGI workshops. Only 45.5% referenced the best practices handout for preparation. Despite this support, 67.3% reported struggling with workshop design and 68.4% with implementation. Notably, 70.2% of facilitators indicated that they would make changes in either the design or implementation of future workshops. CONCLUSIONS: Although most workshop facilitators were content experts, the majority lacked SGI pedagogical skills. Enhanced preworkshop guidance, coupled with opportunities for feedback and coaching, may improve the educational value for facilitators and participants.
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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 | 0.032 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".