Getting physical with medical education: Exercise based virtual anatomy review classes for medical students
Bibliographic record
Abstract
The benefits of regular physical activity are well known. Yet, few studies have examined the effectiveness of integrating physical activity (PA) into curricula within a post-secondary setting. To investigate the incorporation of PA into medical curriculum, we developed a series of optional exercise-based review sessions designed to reinforce musculoskeletal (MSK) anatomy course material. These synchronous sessions were co-taught by a group fitness instructor and an anatomy instructor. The fitness instructor would lead students through both strength and yoga style exercises, while the anatomy instructor asked questions about relevant anatomical structures related to course material previously covered. After the sessions, participants were asked to evaluate the classes on their self-reported exam preparedness in improving MSK anatomy knowledge, PA levels, and mental wellbeing. Thirty participants completed surveys; a majority agreed that the classes increased understanding of MSK concepts (90.0%) and activity levels (97.7%). Many (70.0%) felt that the classes helped reduce stress. The majority of respondents (90.0%) agreed that the classes contributed to increased feelings of social connectedness. Overall, medical students saw benefit in PA based interventions to supplement MSK course concepts. Along with increasing activity levels and promoting health behaviours, integrating PA into medical curriculum may improve comprehension of learning material, alleviate stress and foster social connectivity among medical students.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".