Why physicians teach: giving back by paying it forward
Bibliographic record
Abstract
CONTEXT: Despite the pace and intensity of the in-patient clinical setting, physicians carve out time for teaching medical students and residents. OBJECTIVES: The goal of this study was to explore what it means for physicians to teach students and residents in the in-patient setting. METHODS: We conducted semi-structured interviews with 15 practising physicians from the departments of internal medicine, surgery and paediatrics in three university teaching hospitals at McGill University, using an interpretive phenomenological methodology. RESULTS: Five themes elucidated the meaning of teaching for physicians in the in-patient setting: (i) teaching was perceived as an integral part of their identity; (ii) teaching allowed them to repay former teachers for their own training; (iii) teaching gave them an opportunity to contribute to the development of the next generation of physicians; (iv) teaching enabled them to learn, and (v) teaching was experienced as personally energising and gratifying. Participants were morally and socially motivated to give time and effort through teaching (e.g. to pay forward their own privilege and thereby help to develop the next generation); teaching also gave them a sense of personal fulfilment (e.g. by allowing them to mould young minds and leave a legacy). CONCLUSIONS: This study holds a number of implications for medical education with relevance to the recruitment and retention of clinical teachers, recognition of clinical teaching, and evidence-informed faculty development. The findings also suggest that teaching in an academic setting can bring joy and fulfilment to practising physicians.
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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 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.022 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".