The research contributions of predominantly North American Family Medicine educators to medical learner feedback: a descriptive analysis following a scoping review
Bibliographic record
Abstract
Background and objectives: In 2016, we performed a scoping review as a means of mapping what is known in the literature about feedback to medical learners. In this descriptive analysis, we explore a subset of the results to assess the contributions of predominantly North American family medicine educators to the feedback literature. Methods: Nineteen articles extracted from our original scoping review plus six articles identified from an additional search of the journal Family Medicine are described in-depth. Results: The proportion of articles involving family medicine educators identified in our scoping review is small (n=19/650, 3%) and the total remains low (25) after including additional articles (n=6) from a Family Medicine search. They encompass a broad range of feedback methods and content areas. They primarily originated in the United States (n=19) and Canada (n=3) within Family Medicine Departments (n=20) and encompass a variety of scientific and educational research methodologies. Conclusions: The contributions of predominantly North American Family Medicine educators to the literature on feedback to learners are sparse in number and employ a variety of focus areas and methodological approaches. More studies are needed to assess for areas of education research where family physicians could make valuable contributions.
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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 | 0.043 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.037 | 0.033 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".