A peer‐led kinesthetic forearm and wrist anatomy workshop: A multiple cohort study
Bibliographic record
Abstract
An understanding of forearm and wrist anatomy is necessary for the diagnosis and treatment of various injuries. Evidence supports the use of peer-assisted learning (PAL) as an effective resource for teaching basic science courses. First-year medical students across three class years participated in an optional PAL kinesthetic workshop wherein participants created anatomically accurate paper models of forearm and wrist muscles. Participants completed pre- and post-workshop surveys. Participant and nonparticipant exam performances were compared. Participation ranged from 17.3% to 33.2% of each class; participants were more likely to identify as women than men (p < 0.001). Participants in cohorts 2 and 3 reported increased comfort with relevant content after the workshop (p < 0.001). Survey responses for cohort 1 were omitted due to low response rates; however, exam performances were assessed for all three cohorts. Cohort 2 participants scored higher than nonparticipants on forearm and wrist questions on the cumulative course exam (p = 0.010), while the opposite was found for cohort 3 (p = 0.051). No other statistically significant differences were observed. This is the first study to examine quantitative and qualitative results for a PAL intervention repeated for three separate cohorts. Although academic performance varied, two cohorts reported increased comfort with relevant course material after the workshop. Results of this study support the need for further exploration of PAL workshops as an instructional method in teaching anatomy and highlight the challenges associated with repeating interventions over multiple years. As more studies attempt replication across multiple years, these challenges may be addressed, thereby informing PAL best practices.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".