Defining competency-based evaluation objectives in family medicine: communication skills.
Bibliographic record
Abstract
OBJECTIVE: To provide a pragmatic approach to the evaluation of communication skills using observable behaviours, as part of a multiyear project to develop competency-based evaluation objectives for Certification in family medicine. DESIGN: A nominal group technique was used to develop themes and subthemes and to identify positive and negative observable behaviours that demonstrate competence in communication in family medicine. 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: The group used the nominal group technique to derive a list of observable behaviours that would constitute a detailed operational definition of competence in communication skills; multiple iterations were used until saturation was achieved. The group met several times a year, and membership remained unchanged during the 4 years in which the work was conducted. The iterative process was undertaken twice--once for communication with patients and once for communication with colleagues. MAIN FINDINGS: Five themes, 5 subthemes, and 106 positive and negative observable behaviours were generated. The subtheme of charting skills was defined using a key-features analysis. CONCLUSION: Communication skills were defined in terms of themes and observable behaviours. These definitions were intended to help assess family physicians' competence at the start of independent practice.
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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 | 0.239 | 0.330 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".