Medical Students Learn Professionalism in Near-Peer Led, Discussion-Based Small Groups
Bibliographic record
Abstract
Problem: Medical educators recognize that professionalism is difficult to teach to students in lecture-based or faculty-led settings. An underused but potentially valuable alternative is to enroll near-peers to teach professionalism. Intervention: We describe a novel near-peer curriculum on professionalism developed at Queen’s University School of Medicine. Senior medical students considered role models by their classmates were nominated to facilitate small-group seminars with junior students on topics in professionalism. Each session was preceded by brief pre-readings or prompts and engaged students in semistructured, open-ended discussion. Three 2-hour sessions have occurred annually. Context: The near-peer sessions are a required component (6 hours; 20%) of the 1st-year professionalism course at Queen’s University (30 hours), which otherwise includes faculty-led seminars, lectures, and online modules. Senior facilitators are selected through a peer nomination process during their 3rd year of medical school. This format was chosen to create a highly regarded position to which students could aspire by demonstrating positive professionalism. Outcome: We performed a qualitative descriptive evaluation of the near-peer curriculum. Fifty-six medical students participated in 11 focus group interviews, which were coded and analyzed for themes inductively and deductively. Quantitative reviews of student feedback forms and a third-party thematic analysis were performed to triangulate results. Medical students preferred the near-peer-led discussion-based curriculum to faculty-led seminars and didactic or online formats. Junior students could describe specific examples of how the curriculum had influenced their behavior in academic, clinical, and personal settings. They cited senior near-peer facilitators as the strongest aspect of the curriculum for their social and cognitive congruence. Senior students who had facilitated sessions regarded the peer teaching experience as formative to their own understanding of professionalism. Lessons Learned: Formal medical curricula on professionalism should emphasize near-peer-led small-group discussion as it fosters a nuanced understanding of professionalism for both early level students and senior students acting as teachers.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".