Teaching Opportunities for Family Medicine Residents: Towards a Resident Teaching Framework
Bibliographic record
Abstract
CONTEXT: Family Medicine (FM) residents are the next generation of FM teachers. In Canada, providing teaching opportunities to trainees is an accreditation requirement. While FM residency programs have introduced teaching experiences into their curricula, the full spectrum of available teaching opportunities may not be readily recognized. Mapping resident teaching opportunities to the three domains of the Fundamental Teaching Activities (FTA) Framework (clinical preceptor, teaching outside of the clinical setting, and educational leader) has the potential to identify where residents are currently teaching and in which areas they may need further experience. OBJECTIVE: To identify and describe teaching opportunities that FM residents experience in their training and to map them onto the FTA Framework. STUDY DESIGN AND ANALYSIS: This qualitative study used semi-structured, one-on-one interviews conducted virtually via Zoom. Interviews were recorded, transcribed, and analyzed thematically. Ethics approval was obtained. SETTING: Department of Family Medicine, University of Alberta, Canada. This is a 2-year residency program consisting of both urban and rural sites. POPULATION STUDIED: FM residents and teachers/educational leaders (faculty) in the department were recruited. 10 residents and 12 faculty took part in interviews. INSTRUMENT/OUTCOME MEASURES: Separate interview guides were developed for residents and faculty. Questions addressed opportunities for resident teaching, both formally scheduled as part of the program and those occurring more organically or sought out by residents. Details regarding each opportunity (e.g. setting, learners, content) were also obtained. RESULTS: Teaching opportunities identified by residents and faculty were amenable to mapping onto the FTA Framework. Residents had fewer opportunities in the domain of educational leader than as clinical preceptor or in teaching outside of the clinical setting. Several other areas of interest were identified as emerging themes including engagement strategies, faculty influence on resident teaching, resident motivation to teach, and virtual teaching experiences. CONCLUSIONS: Teaching opportunities of FM residents were described and amenable to mapping onto the FTA Framework. However, there appears to be a need for a teaching framework specific to residents to guide teaching opportunities and resident development as educators.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".