The Effectiveness of a Self-Directed e-Learning Module on Trainee Knowledge and Confidence during Plastic Surgery Clinical Rotations
Bibliographic record
Abstract
Background: Exposure to plastic surgery is limited during medical school. This makes rotations for clinical clerks and off-service residents challenging. Available resources are often too detailed and overwhelming. Having an accessible, concise, and interactive plastic surgery e-learning module reviewing core plastic surgery topics could help prepare incoming trainees for their rotations. Methods: An e-learning module was created using text, images, and in-house recorded video recordings. Two cohorts were recruited: control cohort (n = 9), who completed their plastic surgery rotation without use of the module, and an interventional cohort (n = 18), who completed the rotation with use of the module. A demographic survey, a 20-question multiple-choice knowledge test, and self-reported confidence score were completed by both cohorts at the end of their plastic surgery rotations. The intervention cohort also completed the knowledge test at the beginning of their rotation to establish baseline. Knowledge and confidence scores were compared using two-tailed, unpaired, nonparametric analyses (Mann-Whitney test). Results: Learners from the intervention cohort reported a 95% module completion rate and found the resource “extremely helpful” (average Likert of 4.8/5). Learners indicated that they were very likely to recommend the resource to others (average Likert 4.9/5). The intervention cohort scored significantly higher on the knowledge test compared with the control cohort (P = 0.008), and on average reported higher confidence levels; however, this was not statistically significant (P = 0.057). Conclusion: An accessible and concise module on core plastic surgery concepts enhances learner knowledge and confidence during plastic surgery clinical rotations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".