Patients’ views: improving care for people with fibromyalgia
Bibliographic record
Abstract
AIM: The purpose of this research is to provide healthcare professionals with patients' views on improving care for people with fibromyalgia. BACKGROUND: Fibromyalgia is a chronic pain syndrome that often challenges patients and healthcare professionals. Current literature suggests that the people with fibromyalgia are disempowered by their interactions with physicians. Soliciting patients' suggestions for improving care can be helpful in identifying weaknesses and strengths in current physician-patient interactions, and in improving future care. METHODS: Forty-two participants with fibromyalgia submitted written responses to open-ended questions about their experiences with physicians, and how care could be improved. RESULTS: Positive interactions with physicians referenced care quality, feelings of respect and patient involvement. Negative interactions included receiving inadequate care, perceiving the physician to be uncaring and lacking knowledge of fibromyalgia. CONCLUSIONS: Unlike most research suggests, the majority of patients with fibromyalgia report having positive interactions with physicians. However, suggestions for improving care are similar throughout the literature, reinforcing the need for supportive care, empathetic listening and increased knowledge of alternative treatments. RELEVANCE TO CLINICAL PRACTICE: Patients' recommendations identified in this study can be implemented by nurses involved in providing supportive care and education to patients with fibromyalgia. Application of patients' recommendations may facilitate the creation of therapeutic alliances and improve patients' satisfaction with care.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".