Predictors of Adherence to Treatment in Women With Fibromyalgia
Bibliographic record
Abstract
OBJECTIVES: The goal of this study was to identify predictors of general and medication adherence in women with fibromyalgia (FM). METHODS: Participants were 142 women recruited from tertiary care hospitals or the community and 10 rheumatologists. Participants' demographic, clinical, and psychosocial characteristics, as well as patient-physician discordance, were assessed at the index visit. Adherence was assessed 6 months later. Multivariable generalized estimating equations were used to identify predictors of general adherence and adherence to medication. RESULTS: The average age of participants was 50.9 years (SD=10.2) and the median duration of FM was 32 months. Participants reported extensive use of health services and medications. The mean score for general adherence was 61.0 (SD=22.4; range 0-100) and 52.9% of the cohort reported at least one form of behavior reflecting nonadherence to medications. More general adherence was significantly predicted by lower patient-physician discordance on patient well-being and lower patient psychological distress. Medication adherence was significantly predicted by higher affective pain and lower patient psychological distress. CONCLUSIONS: Adherence is influenced by both clinical (patient-physician discordance and pain) and psychological (distress) factors in women with FM. Improvements in these domains may improve adherence in FM.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".