Role of Peer Learning in Students’ Skill Acquisition and Interest in Plastic Surgery
Bibliographic record
Abstract
Background: Although the number of plastic surgery residency positions increased over the past decade, interest among Canadian medical students experienced the opposite trajectory. The aim of this study was to assess the effect of a low intensity, basic surgical skills workshop on medical students’ confidence and interest in surgery in general, and plastic surgery in particular. Methods: Before and after participating in a 60-minute suturing workshop, preclinical medical students completed a cloud-based questionnaire that evaluated the changes in their suturing confidence and interest in pursuing a career in different surgical subspecialties. Results: Eighty-five medical students (52 females and 33 males), with an average age of 22.9 ± 3.6 years participated in this study. Before the workshop, 95% of participants perceived their suturing ability to be at a beginner’s level and reported that they have not received sufficient suturing training during their medical education to date. Their self-reported confidence in suturing was 1.9 ± 2.1 out of 10. Following the workshop, participants’ confidence in their surgical skills increased by 165% (P < 0.001, partial eta2 = 0.695). Moreover, 82% reported increased interest in a career in surgery associated with their participation in the workshop. Plastic surgery, general surgery, and otolaryngology were the top 3 specialties that experienced an augmented increase in interest following the workshop. Finally, plastic surgery was the specialty perceived as requiring the most surgical skills by the majority of the students. Conclusion: A 60-minute basic skills suturing workshop significantly improved preclinical medical students’ confidence in their surgical skills, and increased their interest in surgery.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".