Teaching-skills training programs for family medicine residents
Bibliographic record
Abstract
OBJECTIVE To review the literature on teaching-skills training programs for family medicine residents and to identify formats and content of these programs and their effects. DATA SOURCES Ovid MEDLINE (1950 to mid-July 2008) and the Education Resources Information Center database (pre-1966 to mid-July 2008) were searched using and combining the MeSH terms teaching , internship and residency, and family practice; and teaching , graduate medical education, and family practice. STUDY SELECTION The initial MEDLINE and Education Resources Information Center database searches identified 362 and 33 references, respectively. Titles and abstracts were reviewed and studies were included if they described the format or content of a teaching-skills program or if they were primary studies of the effects of a teaching-skills program for family medicine residents or family medicine and other specialty trainees. The bibliographies of those articles were reviewed for unidentified studies. A total of 8 articles were identified for systematic review. Selection was limited to articles published in English. SYNTHESIS Teaching-skills training programs for family medicine residents vary from half-day curricula to a few months of training. Their content includes leadership skills, effective clinical teaching skills, technical teaching skills, as well as feedback and evaluation skills. Evaluations mainly assessed the programs’ effects on teaching behaviour, which was generally found to improve following participation in the programs. Evaluations of learner reactions and learning outcomes also suggested that the programs have positive effects. CONCLUSION Family medicine residency training programs differ from all other residency training programs in their shorter duration, usually 2 years, and the broader scope of learning within those 2 years. Few studies on teaching-skills training, however, were designed specifically for family medicine residents. Further studies assessing the effects of teaching-skills training in family medicine residents are needed to stimulate development of adapted programs for the discipline. Future research should also assess how residents’ teaching-skills training can affect their learners’ clinical training and eventually patient care.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".