Developing and Implementing a Community-Based Model of Care for Fibromyalgia: A Feasibility Study
Bibliographic record
Abstract
Background. Fibromyalgia (FM) is a complex disease posing challenges for primary care providers and specialists in its management.Aim. To evaluate the development and implementation of a comprehensive, integrated, community-based model of care for FM.Methods. A mixed methods feasibility study was completed in a small urban centre in southern British Columbia, Canada. Eleven adults with FM and a team of seven health care providers (HCPs) participated in a 10-week intervention involving education, exercise, and sleep management. Monthly “team-huddle” sessions with HCPs facilitated the integration of care. Data included health questionnaires, patient interviews, provider focus group/interviews, and provider surveys.Results. Both patients and HCPs valued the interprofessional team approach to care. Other key aspects included the benefits of the group, exercise, and the positive focus of the program. Effectiveness of the model showed promising results: quality of care for chronic illness, quality of life, and sleep showed significant ( P<0.05 ) differences from baseline to follow-up.Conclusions. Our community-based model of care for FM was successfully implemented. Further testing of the model will be required with a larger sample to determine its effectiveness, although promising results were apparent in our feasibility study.
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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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".