Preliminary trial of an online acceptance-based behavioural treatment for military, police, and Veterans with chronic pain
Bibliographic record
Abstract
Introduction: Chronic pain is a serious health issue in Canada but an even greater issue in military populations. Individuals experiencing chronic pain frequently find attending in-person treatment sessions difficult because of pain flare-ups, discomfort when travelling, and pain-related avoidance behaviours. These challenges function to maintain the pain cycle and prevent engagement in previously enjoyed activities. The purpose of this study was to gather preliminary evidence for the effectiveness of an online acceptance-based behavioural treatment of chronic pain designed specifically for military, police, and Veterans of these forces. Methods: In this preliminary trial, 15 participants engaged in an 8-week online treatment of chronic pain supplemented with optional biweekly group sessions. Participants completed pre- and post-treatment measures relating to key facets of the fear–avoidance model of chronic pain. Results: Participants' scores improved following treatment on measures of pain acceptance, kinesiophobia, and pain catastrophizing, and pain intensity ratings trended in the expected direction. Discussion: These preliminary results support the feasibility of our online acceptance-based treatment of chronic pain when combined with optional biweekly in-person group sessions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".