Assessment of a matched‐pair instrument to examine doctor−patient communication skills in practising doctors
Bibliographic record
Abstract
OBJECTIVES: To develop and psychometrically assess the feasibility, reliability and validity of an assessment tool in which both doctor and patient perceptions of the communication that occurred in a single office visit are captured. METHODS: Two 19-item (5-point scale) questionnaires, with parallel content, were developed for doctor and patient completion following a visit. Both process and content were queried. Family doctors and specialists across Canada were recruited to provide data from 25 visits. We assessed feasibility by examining recruitment and percentages of people 'unable to assess' each item. Evidence for validity was examined through exploratory factor analysis, the correlations between doctor and patient data and linear regression. Reliability was assessed through internal consistency reliability and generalisability coefficient analyses. RESULTS: Data from 1845 doctor-patient dyads (91 doctors) showed similarly high ratings (> 4/5) for both doctors and patients, with few unable-to-assess items. There were low correlations between items and questionnaires. The principle components analysis indicated 2 factors, process and content, accounting for 52% and 7% of the doctor variance and 60% and 6% of the patient variance, respectively. The linear regression showed that only gender accounted for any of the variance in ratings. Cronbach's alphas for both doctor and patient questionnaires were > or = 0.96. The G analysis provided a G = 0.98 and 0.40 (standard errors of 0.003 and 0.02) for doctors and patients, respectively. CONCLUSIONS: The data suggest this is a feasible tool with which to assess communication skills and that there is evidence for its validity and reliability.
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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 | 0.031 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".