Twelve tips for developing a near-peer shadowing program to prepare students for clinical training
Bibliographic record
Abstract
BACKGROUND: One effective way to help prepare medical students for clinical training is the implementation of a near-peer shadowing program, in which pre-clinical trainees shadow clinical trainees. AIMS: This article describes techniques for ensuring the effectiveness of a near-peer shadowing program in the hope of improving the preparedness of students for clinical training. METHOD: A list of 12 tips were developed by combining a review of the literature with a reflection upon the authors' own experiences with developing a near-peer shadowing program, in which first-year medical students shadowed first-year residents. RESULTS: Both successes and failures were identified, both in the literature and in the author's own experiences. These can be used to inform the development of future programs. CONCLUSIONS: A near-peer shadowing program has the strong potential to play a key role in preparing students to enter clinical training. These 12 tips, drawn from the literature and our own experience, will maximize the benefits for both student and tutor learning and minimize the potential pitfalls encountered by other programs.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".