Defining competency-based evaluation objectives in family medicine: professionalism.
Bibliographic record
Abstract
OBJECTIVE: To develop and describe observable evaluation objectives for assessing competence in professionalism, which are grounded in the experience of practising physicians. DESIGN: Modified 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 an iterative process, the expert group defined a list of observable behaviours that are indicative of professionalism, or not, in the family medicine setting. Themes relate to professional behaviour in family medicine; specific observable behaviours are those that family physicians believe are indicative of professionalism for each theme. MAIN FINDINGS: The expert group identified 12 themes and 140 specific observable behaviours to assist in the observation and discussion of professional behaviour in family medicine workplace settings. CONCLUSION: Competency-based education literature emphasizes the importance of formative evaluation and feedback. Such feedback is particularly challenging in the domain of professionalism because of its personal nature and the potential for emotional reactions. Effective dialogue between learners and teachers begins with clear expectations and reference to descriptions of relevant, specific behaviour. This research has generated a competency-based resource to assist the assessment of professional behaviour in family medicine educational 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 | 0.137 | 0.238 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".