Residents as teachers: survey of Canadian family medicine residents.
Bibliographic record
Abstract
OBJECTIVE: To examine Canadian family medicine residents' perspectives surrounding teaching opportunities and mentorship in teaching. DESIGN: A 16-question online survey. SETTING: Canadian family medicine residency programs. PARTICIPANTS: Between May and June 2011, all first- and second-year family medicine residents registered in 1 of the 17 Canadian residency programs as of September 2010 were invited to participate. A total of 568 of 2266 residents responded. MAIN OUTCOME MEASURES: Demographic characteristics, teaching opportunities during residency, and resident perceptions about teaching. RESULTS: A total of 77.7% of family medicine residents indicated that they were either interested or highly interested in teaching as part of their future careers, and 78.9% of family medicine residents had had opportunities to teach in various settings. However, only 60.1% of respondents were aware of programs within residency intended to support residents as teachers, and 33.0% of residents had been observed during teaching encounters. CONCLUSION: It appears that most Canadian family medicine residents have the opportunity to teach during their residency training. Many are interested in integrating teaching as part of their future career goals. Family medicine residencies should strongly consider programs to support and further develop resident teaching skills.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".