From See One Do One, to See a Good One Do a Better One: Learning Physical Examination Skills Through Peer Observation
Bibliographic record
Abstract
BACKGROUND: Learning and mastering the skills required to execute physical exams is of great importance and should be fostered early during medical training. Observing peers has been shown to positively influence the acquisition of psychomotor skills. PURPOSE: The current study investigated the influence of peer observation on the acquisition of psychomotor skills required to execute a physical examination. METHODS: Second-year medical students (N=194) learned the neurological physical examination for low back pain in groups of three. Each student learned and performed the physical examination while the other students observed. Analyses compared the impact of the quantity and the quality of observed performances on students' learning of the physical examination skills. RESULTS: Students benefited from observing peers while they executed their examination. Moreover, observing a high-performing peer increased the acquisition of physical examination skills. CONCLUSIONS: Results suggest that group learning activities that allow students to observe their peers during physical examination should be favored.
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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 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".