Online versus in‐person surgical near‐peer teaching in undergraduate medical education during the COVID‐19 pandemic: A mixed‐methods study
Bibliographic record
Abstract
Abstract Background and Aims The coronavirus disease 2019 (COVID‐19) pandemic stimulated a paradigm shift in medical and surgical education from in‐person teaching to online teaching. It is unclear whether an in‐person or online approach to surgical teaching for medical students is superior. We aim to compare the outcomes of in‐person versus online surgical teaching in generating interest in and improving knowledge of surgery in medical students. We also aim the quantify the impact of a peer‐run surgical teaching course. Methods A six‐session course was developed by medical students and covered various introductory surgical topics. The first iteration was offered online to 70 UK medical students in March 2021, and the second iteration was in‐person for 20 students in November 2021. Objective and subjective knowledge was assessed through questionnaires before and after each session, and also for the entire course. Data were analyzed from this mixed‐methods study to compare the impact of online versus in‐person teaching on surgical knowledge and engagement. Results Students in both iterations showed significant improvement of 33%–282% across the six sessions in knowledge and confidence after completing the course ( p < 0.001). There was no significant difference in the level of objective knowledge, enjoyment, or organization of the course between online and in‐person groups, although the in‐person course was rated as more engaging (mean Likert score 9.1 vs. 9.7, p = 0.033). Discussion Similar objective and subjective surgical teaching outcomes were achieved in both iterations, including in “hands‐on” topics such as suturing, gowning, and gloving. Students who completed the online course did not have any lower knowledge or confidence in their surgical skills; however, the in‐person course was reported to be more engaging. Surgical teaching online and in‐person may be similarly effective and can be delivered according to what is most convenient for the circumstances, such as in COVID‐19.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".