Defining competency-based evaluation objectives in family medicine: dimensions of competence and priority topics for assessment.
Bibliographic record
Abstract
OBJECTIVE: To develop a definition of competence in family medicine sufficient to guide a review of Certification examinations by the Board of Examiners of the College of Family Physicians of Canada. DESIGN: Delphi analysis of responses to a 4-question postal survey. SETTING: Canadian family practice. PARTICIPANTS: A total of 302 family physicians who have served as examiners for the College of Family Physicians of Canada's Certification examination. METHODS: A survey comprising 4 short-answer questions was mailed to the 302 participating family physicians asking them to list elements that define competence in family medicine among newly certified family physicians beginning independent practice. Two expert groups used a modified Delphi consensus process to analyze responses and generate 2 basic components of this definition of competence: first, the problems that a newly practising family physician should be competent to handle; second, the qualities, behaviour, and skills that characterize competence at the start of independent practice. MAIN FINDINGS: Response rate was 54%; total number of elements among all responses was 5077, for an average 31 per respondent. Of the elements, 2676 were topics or clinical situations to be dealt with; the other 2401 were skills, behaviour patterns, or qualities, without reference to a specific clinical problem. The expert groups identified 6 essential skills, the phases of the clinical encounter, and 99 priority topics as the descriptors used by the respondents. More than 20% of respondents cited 30 of the topics. CONCLUSION: Family physicians define the domain of competence in family medicine in terms of 6 essential skills, the phases of the clinical encounter, and priority topics. This survey represents the first level of definition of evaluation objectives in family medicine. Definition of the interactions among these elements will permit these objectives to become detailed enough to effectively guide assessment.
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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 | 0.054 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".