Physician verbal compliance-gaining strategies and patient satisfaction
Bibliographic record
Abstract
Purpose The patient healthcare experience is a complex phenomenon, as is encouraging patient compliance with medical advice. To address this multifaceted relationship, the purpose of this paper is to explore the ways resident physicians verbally encourage patient compliance and the relationship between these compliance-seeking strategies and patient satisfaction. Design/methodology/approach A total of 40 medical interviews between resident physicians and patients were audio-recorded, transcribed, coded, and analysed. Patient questionnaires were also administered and analysed. Findings It was found that resident physicians used indirect orders most frequently, followed by motivation, persuasion, scheduling, and direct orders. It was also found that female patients received (marginally) more messages than male patients; female residents used more messages with female patients than with male patients; female residents used more persuasion messages with female patients than with male patients; male residents were less likely than female residents to use motivational messages with female patients; and compliance was significantly correlated with expertise satisfaction, overall satisfaction, and communication satisfaction. Originality/value This study advances existing research by examining various ways in which residents verbally encourage patient compliance and the relationship between these messages and patient satisfaction. Findings can be used to inform physicians on strategies to encourage patient adherence to medication regimen, appointments, and lifestyle changes.
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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 | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".