Defining competency-based evaluation objectives in family medicine: key-feature approach.
Bibliographic record
Abstract
OBJECTIVE: To develop key features for priority topics previously identified by the College of Family Physicians of Canada that, together with skill dimensions and phases of the clinical encounter, broadly describe competence in family medicine. DESIGN: Modified nominal group methodology, which was used to develop key features for each priority topic through an iterative process. SETTING: The College of Family Physicians of Canada. 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 family medicine context with respect to region, sex, language, community type, and experience. METHODS: The group used a modified Delphi process to derive a detailed operational definition of competence, using multiple iterations until consensus was achieved for the items under discussion. The group met 3 to 4 times a year from 2000 to 2007. MAIN FINDINGS: The group analyzed 99 topics and generated 773 key features. There were 2 to 20 (average 7.8) key features per topic; 63% of the key features focused on the diagnostic phase of the clinical encounter. CONCLUSION: This project expands previous descriptions of the process of generating key features for assessment, and removes this process from the context of written examinations. A key-features analysis of topics focuses on higher-order cognitive processes of clinical competence. The project did not define all the skill dimensions of competence to the same degree, but it clearly identified those requiring further definition. This work generates part of a discipline-specific, competency-based definition of family medicine for assessment purposes. It limits the domain for assessment purposes, which is an advantage for the teaching and assessment of learners. A validation study on the content of this work would ensure that it truly reflects competence 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 | 0.099 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".