The dynamics of resident?patient communication: Data from Canada
Bibliographic record
Abstract
The objectives of this study were to examine patterns of resident-patient communication and the relationship between resident patterns of speech with patient satisfaction. Forty consultations, ten in each of the four gender combinations (male resident/male patient, male resident/female patient, female resident/female patient, female resident/male patient) were audiotaped and microanalyzed using the Roter Interaction Analysis System. Several findings depart significantly from previous studies with physician-only or physician-resident-mixed samples. First, the average length of the 40 consultations was 19.5 minutes, 11.3 minutes longer than consultations in a physician-only sample drawn in the same clinic previously. Second, male residents engaged in twice as much psychosocial talk as female residents and conducted longer consultations. Third, residents asked 80% of the total questions while patients asked 20% of the questions. Previous studies with physician-only or physician-resident-mixed samples reported that physicians ask 89-99% of the total questions. Finally, patients' overall satisfaction and communication satisfaction were negatively correlated with residents' positive talk, which constitutes 31% of a given resident's total utterances. In the study conducted in the same clinic with a physician-only sample, physician positive talk was 26% and physician positive talk was not correlated with patient satisfaction. Is this a signal that residents should reduce the amount of positive talk? Apparently more studies with resident-only samples are needed to answer this and other unanswered questions in the field to offer directives to resident training.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".