Near‐peer question writing and teaching programme
Bibliographic record
Abstract
BACKGROUND: Near-peer assisted learning (NPAL) is an increasingly important tool in medical education; however, although numerous published papers discuss its merits, the evidence on the effectiveness and student perception of NPAL is limited. We describe a novel near-peer question writing and teaching programme to assess whether it improves the confidence of first-year medical students for their first In-Course Assessment (ICA) in medical school. The evidence on the effectiveness and student perception of NPAL is limited METHODS: A team of medical students designed a question development procedure and a structured teaching programme. A total of 280 first-year medical students were invited to appraise the questions. A questionnaire assessing confidence and student perception was sent to participants at different time points leading up to and after their first ICA at the medical school. Statistical analysis was performed using spss 20. RESULTS: One hundred and seventy one students attempted the questions. Students felt more confident with short-answer questions (SAQs; 95% CI 1.5-2.0, p < 0.05) and multiple-choice questions (MCQs; 95% CI 1.0-1.5, p < 0.05), as assessed using the Wilcoxon signed-rank test. Overall, students were satisfied with the NPAL questions and teaching programme following their university examinations (p > 0.01). CONCLUSION: The NPAL project highlighted a trend towards improving students' confidence. Furthermore, the question writing and teaching programme can be used as a guide to confidently hold teaching sessions in the future. The NPAL project further reinforces existing published papers that have shown NPAL to be a powerful adjunct to existing undergraduate 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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".