Common concepts in separate domains? Family physicians’ ways of understanding teaching patients and trainees, a qualitative study
Bibliographic record
Abstract
BACKGROUND: Medical education is increasingly expanding into new community teaching settings and the need for clinical teachers is rising. Many physicians taking on this new role are already skilled patient educators. The purpose of this research was to explore how family physicians conceptualize teaching patients compared to the teaching of trainees. Our aim was to understand if there is any common ground between these two roles in order to support faculty development based on already existing skills. METHODS: Semi-structured interviews with twenty-five family physician preceptors were conducted in Vancouver, Canada and thematically analyzed. RESULTS: We identified four key areas of overlap between the two fields (being learner-centered; supporting the acquisition, application and integration of knowledge; role modeling and self-disclosure; and facilitating autonomy) and three areas of divergence (aim of teaching and setting the learning objectives; establishing rapport; and providing feedback). CONCLUSIONS: Finding common ground between these two teaching roles would support knowledge translation and inquiry between the domains of teaching patients and trainees. It would furthermore open up new avenues for improving training and practice for clinical teachers by better linking faculty development and continuing medical education (CME).
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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 | 0.022 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".