Learning how to suture: Should learners observe a demonstration of someone who is experienced or inexperienced to improve their own performance?
Bibliographic record
Abstract
BACKGROUND: We explored the influence of observing experienced or inexperienced demonstrators on a learner's own performance of a simple, interrupted suture. METHODS: Participants without suturing experience observed videos of an experienced or inexperienced demonstrator suturing, rated the performance, and then practiced the task; this was repeated twice more. Suturing performance was rated using a modified Objective Structured Assessment of Technical Skills (OSATS). We calculated participants' accuracy in rating the demonstrator's performances. Data were analyzed using mixed model analyses of variance (ANOVAs) and pairwise t-tests. RESULTS: Participants who observed the experienced demonstrator significantly improved their suturing performance. Participants who observed the inexperienced demonstrator became significantly more accurate at rating the demonstrator's performance. CONCLUSIONS: Learners who are new to suturing can improve their suturing performance through observation of an experienced demonstrator and physical practice. While the experiment itself was conducted in-person, learners could engage in this method of learning remotely.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".