Not Just Us Lecturing at You: Evaluating Small Group Workshops at a National Surgical Conference
Bibliographic record
Abstract
BACKGROUND: Academic conferences typically rely on traditional lectures, which limit learner engagement. A national surgical conference recently implemented a novel session format "Breakshops," which focused on small group instruction (SGI). We assessed participants' perceptions of these sessions, as well as instructional quality and interactivity. STUDY DESIGN: Breakshops were implemented at the 2024 American Pediatric Surgical Association (APSA) annual meeting. Each 45-minute concurrent workshop was intended for a few dozen participants and incorporated SGI rather than slide-based presentations. Sessions were proposed, designed, and facilitated by attendees. We performed a convergent parallel mixed-methods analysis. Semi-structured interviews of participants were recorded, transcribed, inductively coded, and thematically analyzed. Quantitative data were collected from surveys of participants assessing satisfaction and perceived value and Program Committee members assessing SGI feature use and learner interactivity. RESULTS: Thirty-one (70.5%) of 44 Breakshops were assessed by the Program Committee. From sixteen interviews and corresponding quantitative results, three themes emerged. First, Breakshops were a unique and valued addition to conference programming, reflected in a mean satisfaction score of 8.1 of 10 and 96.3% of 895 ratings deeming "Valuable" or "Somewhat Valuable." Second, Breakshops enhanced active learning, with increased use of SGI features correlating with greater interaction (p = 0.01) and value (p = 0.04). Third, there were opportunities to refine Breakshop implementation at a programmatic level. CONCLUSION: Small group workshops which emphasize interaction and active learning enhanced learner engagement and provided unique value at the 2024 APSA conference. Their broader adoption should be considered to improve conference learning experiences.
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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 | 0.021 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".