Evaluation of Online Near-Peer Teaching for Penultimate-Year Objective Structured Clinical Examinations in the COVID-19 Era: Longitudinal Study
Bibliographic record
Abstract
BACKGROUND: The benefits of near-peer learning are well established in several aspects of undergraduate medical education including preparing students for Objective Structured Clinical Examinations (OSCEs). The COVID-19 pandemic has resulted in a paradigm shift to predominantly online teaching. OBJECTIVE: This study aims to demonstrate the feasibility and benefits of an exclusively online near-peer OSCE teaching program in a time of significant face-to-face and senior-led teaching shortage. METHODS: A teaching program was delivered to penultimate-year students by final-year students at Manchester Medical School. Program development involved compiling a list of salient topics and seeking senior faculty approval. Teachers and students were recruited on Facebook. In total, 22 sessions and 42 talks were attended by 72 students and taught by 13 teachers over a 3-month period. Data collection involved anonymous weekly questionnaires and 2 separate anonymous student and teacher postcourse questionnaires including both quantitative and qualitative components. RESULTS: On a scale of 1-10, students rated the quality of the program highly (mean 9.30, SD 1.15) and felt the sessions were highly useful in guiding their revision (mean 8.95, SD 0.94). There was a significant increase in perceived confidence ratings after delivery of the program (P<.001). Teachers felt the program helped them better understand and retain the subject material taught (mean 9.36, SD 0.81) and develop skills to become effective clinical teachers (mean 9.27, SD 0.79). CONCLUSIONS: This is the first study demonstrating the efficacy of a near-peer OSCE teaching program delivered exclusively online. This provides an exemplary framework for how similar programs should be encouraged given their efficacy and logistical viability in supplementing the undergraduate curriculum.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".