Génération d’hypothèses diagnostiques par l’observation engagée des pairs: une étude descriptive quantitative en contexte de simulation clinique
Bibliographic record
Abstract
Background: Learning clinical reasoning (CR) requires practice in a variety of educational settings. As part of the clinical simulation sessions at the University of Ottawa's Faculty of Medicine, pre-clerkship students are paired in dyads to increase the number of practical clinical cases before the clerkship. One student plays the role of a Clinical Student (CS) and the other alternates as a Student Observer (SO). This quantitative descriptive study aims to compare the diagnostic hypothesis generation by SOs with that of CSs to support the usefulness of engaged peer observation as a CR learning strategy in clinical simulation settings. Methods: Following an interview with a simulated patient, CSs and SOs were asked to generate two diagnostic hypotheses in an electronic form. Responses were compiled, categorized, and compared in terms of equivalent diagnostic hypotheses within the same dyad. The difference in frequency distribution of equivalent hypotheses was statistically analyzed using a chi-square calculation. Results: < 0.01). Conclusion: SOs appear to be able to generate diagnostic hypotheses similar to those of CSs. The results support the use of engaged peer observation as a learning strategy for CR in clinical simulation settings in pre-clerkship medical education.
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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 | 0.053 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".