Are nonphysician health care providers prepared and supported to teach in family medicine?
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Understanding how nonphysician health care providers (NPHCPs) teach medical trainees is integral to optimizing family medicine education. The objective of this study was to examine the teaching roles, level of preparation and support, and the challenges encountered by NPHCPs. METHODS: A cross-sectional web-based survey of NPHCPs was conducted across academic teaching units affiliated with the University of Toronto's Department of Family and Community Medicine (DFCM). The level of preparation for educational roles, perceived support, challenges encountered, and educational training needs of NPHCPs were examined. Variables associated with preparedness to teach were also identified. RESULTS: Of the 193 NPHCPs surveyed, 166 (86%) completed the questionnaire. A total of 126 (82%) of NPHCP educators (nurses, social workers, dietitians, and pharmacists) reported teaching medical trainees. Most did not hold faculty appointments. The majority had no formal training in teaching, and less than half felt prepared for their academic responsibilities. NPHCPs perceived a lack of support for their teaching. NPHCPs also identified predictable challenges such as lack of time and lack of funding. Challenges specific to cross-professional teaching were also identified. NPHCPs expressed an interest in receiving continuing education to improve their teaching skills. NPHCPs' self-reported level of preparedness to teach was variable and associated with years of teaching experience, information received about trainees, challenges faced, and continuing education needs. CONCLUSIONS: NPHCPs are extensively involved in teaching medical trainees. There is variability in their preparation level, and they encounter significant challenges. To advance effective and sustainable inter-professional education (IPE) within family medicine, addressing these issues is crucial.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".