Examination of an internet-delivered cognitive behavioural pain management course for adults with fibromyalgia: a randomized controlled trial
Bibliographic record
Abstract
Fibromyalgia (FM) is a common and often debilitating chronic pain condition. Research shows that symptoms of depression and anxiety are present in up to three quarters of individuals with FM. Of concern, most adults with FM cannot access traditional face-to-face cognitive behavioural pain management programs, which are known to be beneficial. Given known difficulties with treatment access, the present study sought to explore the efficacy and acceptability of a previously developed Internet-delivered cognitive behavioural pain management course, the Pain Course, for adults with FM. The five-lesson course was delivered over eight weeks and was provided with brief weekly contact, via telephone and secure email, with a guide throughout the course. Participants were randomized either to the Pain Course (n = 30) or to a waiting-list control group (n = 30). Symptoms were assessed at pre-treatment, post-treatment and 4-week follow-up. Completion rates (87%) and satisfaction ratings (86%) were high. Improvements were significantly greater in treatment group participants compared to waiting-list group participants on measures of FM (Cohen's d =.70; 18% reduction), depression (Cohen's d =.63-.72; 20-28% reduction), pain (Cohen's d =.87; 11% improvement) and fear of pain (Cohen's d =1.61; 12% improvement). Smaller effects were also observed on measures of generalized anxiety and physical health. The changes were maintained at four-week follow-up. The current findings add to existing literature and highlight the specific potential of Internet-delivered cognitive behavioural pain management programs for adults with FM, especially as a part of stepped-care models of care. Future research directions are described.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".