Knowledge of CanMEDS–Family Medicine roles
Bibliographic record
Abstract
Objective This study evaluates the self-perceived awareness of the new CanMEDS–Family Medicine (CanMEDS-FM) roles by family medicine residents. Design A 22-question online survey. Setting Canadian family medicine residency programs. Participants All residents enrolled in a Canadian family medicine residency as of September 2010 received the survey between May and June 2011. A total of 568 residents participated. Main outcome measures Survey respondents indicated their awareness of, their exposure to, and the perceived importance of the CanMEDS-FM roles. Results The survey response rate was 25.1%. In total, 88.9% (463 of 521) of family medicine residents were aware of the CanMEDS-FM roles; there was no statistically significant difference in awareness between first- and second-year residents. Family medicine expert and communicator were most frequently chosen as the most important CanMEDS-FM roles, while manager and scholar were selected the least often. Overall, 76.4% of family medicine residents thought that their core family medicine teaching was guided by CanMEDS-FM, while 41.8% thought the same about off-service rotations. Conclusion It appears that most family medicine residents are aware of the CanMEDS-FM roles. While core family medicine training and evaluation seem to be grounded in CanMEDS-FM, residency program directors should endeavour to ensure that the same principles apply during off-service rotations.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".