Predictors of Adherence to an Integrated Multimodal Program for Fibromyalgia
Bibliographic record
Abstract
OBJECTIVE: To describe treatment adherence to a multimodal integrated program for patients with fibromyalgia (FM), identify predictors of adherence to treatment recommendations, and examine the relationship between adherence and patient outcomes. METHODS: Sixty-three patients with FM were followed while participating in a 3-month outpatient program including physiotherapy, occupational therapy, nursing, and cognitive-behavior therapy. Patients completed a battery of psychosocial questionnaires pre- and post-treatment. At the end of each month of the treatment, patients completed 2 adherence questionnaires (for general and specific adherence) and 1 questionnaire about barriers to adherence to treatment. Generalized estimating equations extension of multivariable linear regression analyses for repeated measures examined predictors of general and specific adherence. Conventional linear regression analyses examined the relationships of general adherence with post-treatment FM disability and pain intensity. RESULTS: In general, adherence to treatment recommendations was good (mean general adherence score of 62 points, on a 0 to 100 scale), with no significant changes in mean level of general or specific adherence over the 3-month period. The main predictor for both general and specific adherence was barriers to adherence to treatment. Increased general adherence was significantly associated with lower pain at post-treatment. CONCLUSION: The items described in the questionnaire for barriers to treatment are the main problem when it comes to adhering to a multimodal treatment program for FM. Healthcare professionals are advised to discuss these barriers directly with patients and assist in overcoming them.
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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 | 0.006 |
| 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) | 0.002 | 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".