Verbal compliance-gaining strategies used by male physicians and patient healthcare experience
Bibliographic record
Abstract
This study explores male physicians' use of verbal compliance gaining strategies to encourage patients to adhere to medication regimens, lifestyle changes, or future appointments, and assesses which strategies are associated with patients' reported healthcare experiences. Five physicians from a family practice clinic in northern British Columbia, Canada, were audio-recorded while interacting with 31 patients during actual consultations. Compliance-gaining utterances were coded into five categories of strategies, while patient experience with care was assessed using a questionnaire. A number of intriguing findings emerged: direct orders were related to a more negative experience with interpersonal aspects of care, but were fairly frequently used, especially with female patients. Persuasion was the only strategy that promoted a positive patient experience, but was rarely used. However, the effect of persuasion on patient experience was no longer significant when adjusting for patients' health status. Physicians relied mostly on motivation strategies to encourage adherence, but these strategies were not related to patients' assessment of their healthcare experiences. These results suggest that the most frequently used verbal compliance gaining strategies by physicians are not always appreciated by patients. To be more effective, it is necessary to inform physicians about which compliance-gaining strategies promote a positive patient healthcare experience.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".