Use of behavioural activation to manage pain: a systematic scoping review
Bibliographic record
Abstract
Background: Behavioural activation (BA) is an effective treatment for depression; however, it is unclear if it can be used to manage pain. Objectives: To conduct a scoping review of primary research that reported using BA to support people living with chronic pain to understand how BA had been used in relation to pain. In addition, we wanted to understand whether there were any reported changes in that pain, and how and who delivered BA. Eligibility criteria: Primary research published in English. Sources of evidence We searched seven databases MEDLINE, Ovid Embase, Ovid Emcare, PsycINFO, CINAHL, Scopus and Web of Science, for primary research. No initial date limit was used with the date the searches were conducted used as the end date limit (1 July 2021). Charting methods: A customised data extraction table was developed, piloted and used. Results: 551 papers were screened for inclusion, with 15 papers included in our review. Studies were conducted in North America and in Canada. These included three case studies, nine uncontrolled trials and three randomised controlled trials. Only two studies reported pain as the primary outcome. BA was applied across a range of pain related conditions. The dose of BA ranged from 3 to 16 sessions. Duration of treatment was 3 weeks to 12 months. Most studies reported reductions in pain following exposure to BA. Conclusion: BA has the potential to reduce pain. Caution needs to be exercised in the interpretation of these findings as a high risk of bias was observed in most studies. High-quality research is required to test if BA is an effective intervention for 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 | 0.029 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 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".