Extended family medicine training: Measuring training flows at a time of substantial pedagogic change.
Bibliographic record
Abstract
OBJECTIVE: To examine trends in family medicine training at a time when substantial pedagogic change is under way, focusing on factors that relate to extended family medicine training. DESIGN: Aggregate-level secondary data analysis based on the Canadian Post-MD Education Registry. SETTING: Canada. PARTICIPANTS: All Canadian citizens and permanent residents who were registered in postgraduate family medicine training programs within Canadian faculties of medicine from 1995 to 2013. MAIN OUTCOME MEASURES: Number and proportion of family medicine residents exiting 2-year and extended (third-year and above) family medicine training programs, as well as the types and numbers of extended training programs offered in 2015. RESULTS: The proportion of family medicine trainees pursuing extended training almost doubled during the study period, going from 10.9% in 1995 to 21.1% in 2013. Men and Canadian medical graduates were more likely to take extended family medicine training. Among the 5 most recent family medicine exit cohorts (from 2009 to 2013), 25.9% of men completed extended training programs compared with 18.3% of women, and 23.1% of Canadian medical graduates completed extended training compared with 13.6% of international medical graduates. Family medicine programs vary substantially with respect to the proportion of their trainees who undertake extended training, ranging from a low of 12.3% to a high of 35.1% among trainees exiting from 2011 to 2013. CONCLUSION: New initiatives, such as the Triple C Competency-based Curriculum, CanMEDS-Family Medicine, and Certificates of Added Competence, have emerged as part of family medicine education and credentialing. In acknowledgment of the potential effect of these initiatives, it is important that future research examine how pedagogic change and, in particular, extended training shapes the care family physicians offer their patients. As part of that research it will be important to measure the breadth and uptake of extended family medicine training 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".