Defining competency-based evaluation objectives in family medicine: procedure skills.
Bibliographic record
Abstract
OBJECTIVE: To develop evaluation objectives for assessing competence in procedure skills using a key-features approach. This was part of a multiyear project to develop competency-based evaluation objectives for Certification in Family Medicine. DESIGN: Nominal group technique. SETTING: The College of Family Physicians of Canada in Mississauga, Ont. PARTICIPANTS: An expert group of 7 family physicians and 1 educational consultant, all of whom had experience in assessing competence in family medicine. Group members represented the Canadian context with respect to region, sex, language, community type, and experience. METHODS: Using a nominal group technique, the expert group developed the general key features for procedure skills. The expert group also linked the key features to already established skill dimensions in the domain of competence, to the 4 principles of family medicine, and to the CanMEDS roles. MAIN FINDINGS: The general key features were developed after 5 iterations. Ten key features were outlined and were shown to reflect all the essential skill dimensions in the domain of competence for family medicine. The key features were linked to 2 of the 4 principles of family medicine and to 4 of the CanMEDS roles. CONCLUSION: The general key features for procedure skills were developed to assess competence in procedure skills in family medicine.
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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 | 0.109 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".