A digital peer-to-peer learning platform for clinical skills development.
Bibliographic record
Abstract
BACKGROUND: Due to constraints in time and resources, medical curricula may not provide adequate opportunities for pre-clerkship students to practice clinical skills. To address this, medical students at the University of Alberta developed a digital peer-to-peer learning initiative. The initiative assessed if students can learn clinical skills from their peers in co-curricular practice objective structured clinical exams (OSCEs). METHODS: A total of 144 first-year medical students participated. Students wrote case scenarios that were reviewed by physicians. Students enacted the cases in practice OSCEs, acting as the patient, physician, and evaluator. Verbal and electronic evaluations were completed. A digital platform was used to automate the process. Surveys were disseminated to assess student perceptions of their experience. RESULTS: Seventy-five percent of participants said they needed opportunities to practice patient histories and physical exams in addition to those provided in the medical school curriculum. All participants agreed that the co-curricular practice OSCEs met this need. The majority of participants also agreed that the digital platform was efficient and easy to use. CONCLUSION: Students found the practice OSCEs and digital platform effective for learning clinical skills. Thus, peer-to-peer learning and computer automation can be useful adjuncts to traditional medical curricula.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".