Exploring patient views of empathic optimistic communication for osteoarthritis in primary care: a qualitative interview study using vignettes
Bibliographic record
Abstract
BACKGROUND: Osteoarthritis (OA) causes pain and disability. An empathic optimistic consultation approach can improve patient quality of life, satisfaction with care, and reduce pain. However, expressing empathic optimism may be overlooked in busy primary care consultations and there is limited understanding of patients' views about this approach. AIM: To explore patients' perspectives on clinician communication of empathy and optimism in primary care OA consultations. DESIGN & SETTING: = 33) aged >45 years with hip or knee OA from GP practices in Wessex (Hampshire, Dorest, Wiltshire, and Somerset). METHOD: Fifteen participants watched two filmed OA consultations with a GP, and 18 participants read two case vignettes. In both formats, one GP depicted an empathic optimistic approach and one GP had a 'neutral' approach. Semi-structured interviews were conducted with all participants and analysed using thematic analysis. RESULTS: Patients recognised that empathic communication enhanced interactions, helping to engender a sense of trust in their clinician. They felt it was acceptable for GPs to convey optimism only if it was realistic, personalised, and embedded within an empathic consultation. Discussing patients' experiences and views with them, and conveying an accurate understanding of these experiences improves the credibility of optimistic messages. CONCLUSION: Patients value communication with empathy and optimism, but it requires a fine balance to ensure messages remain realistic and trustworthy. Increased use of a realistic optimistic approach within an empathic consultation could enhance consultations for OA and other chronic conditions, and improve patient outcomes. Digital training to help GPs implement these findings is being developed.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".