Teaching professionalism to first year medical students using video clips
Bibliographic record
Abstract
BACKGROUND: Medical schools are confronted with the challenge of teaching professionalism during medical training. The aim of this study was to examine medical students' perceptions of using video clips as a beneficial teaching tool to learn professionalism and other aspects of physicianship. METHODS: As part of the longitudinal Physician Apprenticeship course at McGill University, first year medical students viewed video clips from the television series ER. The study used qualitative description and thematic analysis to interpret responses to questionnaires, which explored the educational merits of this exercise. RESULTS: Completed questionnaires were submitted by 112 students from 21 small groups. A major theme concerned the students' perceptions of the utility of video clips as a teaching tool, and consisted of comments organized into 10 categories: "authenticity and believability", "thought provoking", "skills and approaches", "setting", "medium", "level of training", "mentorship", "experiential learning", "effectiveness" and "relevance to practice". Another major theme reflected the qualities of physicianship portrayed in video clips, and included seven categories: "patient-centeredness", "communication", "physician-patient relationship", "professionalism", "ethical behavior", "interprofessional practice" and "mentorship". CONCLUSIONS: This study demonstrated that students perceived the value of using video clips from a television series as a means of teaching professionalism and other aspects of physicianship.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".