Assessing communication competence: a review of current tools.
Bibliographic record
Abstract
BACKGROUND: The assessment of communication competence has become a major priority of medical educational, policy, and licensing organizations in the United States and Canada. Multiple tools are available to assess communication competence, but there are few studies that compare the tools. METHODS: A consensus panel of six family medicine educators evaluated 15 instruments measuring the physician-patient interview. The primary evaluation criteria came from the Kalamazoo Consensus Statement (KCS), which derived from a multidisciplinary panel of experts that defined seven essential elements of physician-patient communication. We evaluated psychometric properties of the instruments and other assessment criteria felt to be important to family physicians (exploring family issues, interview efficiency, and usability/practicality). RESULTS: Instruments that received the highest ratings on KCS elements were designed for faculty raters and varied in their practicality/usability ratings and psychometric properties. Few instruments were rated high on psychometric properties or exploring family issues. CONCLUSIONS: The process successfully reviewed and provided a framework for assessing communication skills instruments. There is a need to expand the study, including use of a larger cohort of reviewers to provide more validity to the results and minimize potential biases.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".