Fundamental Teaching Activities in Family Medicine Framework: Analysis of Awareness and Utilization
Bibliographic record
Abstract
INTRODUCTION: In 2015, the College of Family Physicians of Canada, in performing their commitment to supporting its members in their educational roles, created the Family Medicine Framework (FTA). It was designed to assist family medicine educators with an understanding of the core activities of educators: precepting, coaching, and teaching within or beyond clinical settings. Given that an examination of member awareness of FTA has not been previously undertaken, our primary objective was to conduct an evaluation on its utility and application. METHODS: In partnership with College of Family Physicians of Canada Faculty Development Education Committee members, we used a practical participatory evaluation approach to conduct a two-phase mixed-methods evaluation of the FTA. We distributed an electronic survey in French and English languages to Canadian faculty development, program, and site directors in family medicine. We then conducted follow-up interviews with self-selected participants. RESULTS: Of the target populations, 12/15 (80%) faculty development directors (FDDs), 12/18 (66.7%) program directors, and 34/174 (19.5%) site directors completed the electronic survey. Subsequently, 6 FDDs, 3 program directors, and 3 site directors completed an interview (n = 12). Findings indicate that awareness of the FTA was highest among FDDs. Facilitators who encourage teachers to use the FTA and barriers for low uptake were also identified. DISCUSSION: This evaluation illuminated that varied levels of awareness of the FTA may contribute to the low uptake among education leaders. We also suggest future research to address possible barriers that hinder effective applications of the FTA in faculty development initiatives.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".