A scoping review of medical education research in family medicine
Bibliographic record
Abstract
BACKGROUND: Little is known about the state of education research within family medicine. As family medicine education models develop, it is important to develop an understanding of the current state of this research and develop ways to advance the field. METHODS: We conducted a scoping review of family medicine education research to describe 1) research topic areas and 2) the methodologies and methods used to study these topics. MEDLINE, Social Sciences Abstracts and ERIC electronic databases were searched. 817 full text articles from 2002 to 2012 were screened; 624 articles were included in the review. RESULTS: The following research topic areas were identified: continuing education, curriculum development, undergraduate education, teaching methods, assessment techniques, selection of entrants, non-clinical skills, professional and faculty development, clinical decision-making and resident well-being. Quantitative studies comprised the large majority of research approaches; overall minimal methodological details were provided. CONCLUSIONS: Our review highlights an overall need for increased sophisticated in methodological approaches to education research in family medicine, a problem that could be ameliorated by multiple strategies including better engagement of methodologists throughout the research process. The results provide guidance for future family medicine education research programs.
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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 | 0.023 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.028 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".