Transatlantic Peer-to-Peer Learning
Bibliographic record
Abstract
INTRODUCTION: Peer-to-peer learning is a well-established learning modality, which has been shown to improve learning outcomes, with positive implications for clinical practice. The purpose of this pilot study was to explore the feasibility of linking students from North America and Europe with a peer-to-peer learning approach. METHODS: Face and content validity studies were completed on the previously designed and validated online repository http://www.pilgrimshospital.com. Four medical students from the University of Toronto, Canada, were paired with four students from University College Cork, Ireland. Each student was invited to upload two pieces of information learned from a senior colleague that day. Each student was asked to review the information uploaded by their partner, editing with references if needed. Quantitative and qualitative evaluations of the e-peer system were conducted. RESULTS: Over the study period, the system recorded a total of 10 079 individual page views. Questionnaires completed by participants demonstrated that 6/8 found the system either "very easy" or "easy" to use, whereas all found that the system promoted evidenced-based and self-directed learning. Structured interviews revealed 3 main themes: The Peer Connection, Trust in Data Veracity, and Aid to Clinical Learning. CONCLUSION: This pilot study demonstrates it is feasible to link students from separate continents in a community of peer-to-peer learning. This is viewed positively by students and enhances evidenced-based learning, and the aspect of peer connectivity was important to participating students. Such an approach encourages peer cooperation and has the potential to disseminate key clinical learning experiences widely.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.012 |
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".