Evaluation of a therapist-guided virtual psychological pain management program when provided as routine care: a prospective pragmatic cohort study
Bibliographic record
Abstract
INTRODUCTION: Numerous randomized controlled trials have evaluated the outcomes of internet-delivered psychological pain management programs (PMPs) as a way of increasing access to care for people with chronic pain. However, there are few reports of the effectiveness of these PMPs when provided as part of routine care. METHODS: The present study sought to report the clinical and demographic characteristics of users (n = 1367) and examine the effectiveness of an established internet-delivered psychological PMP program in improving several pain-related outcomes, when offered at a national digital mental health service over a 5-year period. It also sought to comprehensively explore predictors of treatment commencement, treatment completion, and clinical improvement. RESULTS: Evidence of clinical improvements (% improvement; Hedges g) were found for all outcomes, including pain interference (18.9%; 0.55), depression (26.1%; 0.50), anxiety (23.9%; 0.39), pain intensity (12.8%; 0.41), pain self-efficacy (-23.8%; -0.46) and pain-catastrophizing (26.3%; 0.56). A small proportion of users enrolled but did not commence treatment (13%), however high levels of treatment completion (whole treatment = 63%; majority of the treatment = 75%) and satisfaction (very satisfied = 45%; satisfied = 37%) were observed among those who commenced treatment. There were a number of demographic and clinical factors associated with commencement, completion and improvement, but no decisive or dominant predictors were observed. DISCUSSION: These findings highlight the effectiveness and acceptability of internet-delivered psychological PMPs in routine care and point to the need to consider how best to integrate these interventions into the pathways of care for people with chronic pain.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".