Brief Psychological Interventions for Reducing Prescription Opioid Use, Related Harm, and Pain Intensity in Patients With Chronic Pain
Bibliographic record
Abstract
OBJECTIVES: Brief psychological interventions (BPIs) have demonstrated effectiveness in reducing substance use and related harm. No systematic review has examined their potential to reduce or prevent prescription opioid use or related harm, and/or pain intensity in opioid-using patients with chronic noncancer pain (CNCP). Recognizing the importance of patient preferences in evidence-based practice, we also sought to assess patient interest in BPIs. MATERIALS AND METHODS: A systematic review of studies published between 1980 and 2020 was conducted using 5 databases. Eligible treatment studies included patients ≥18 years old, with CNCP, and who were using prescription opioids. An adjunctive study independent of our review was also undertaken in 188 prescription opioid-using patients (77% female; Mage=49 y) diagnosed with CNCP. Patients completed pain-related questionnaires online and indicated if they would consider BPI treatment options. RESULTS: The review identified 6 studies. Given the heterogeneity across studies, a meta-analysis was not conducted. A narrative review found that all of the 6 studies demonstrated some evidence for BPI effectiveness for reducing opioid use or related harms; these were assessed as having mostly low methodological quality. Mixed support for the effect on pain intensity was found in 1 study. Despite the inconclusive findings and heterogenous studies identified in the review, 92% of patients in our survey reported interest in receiving a BPI. DISCUSSION: In combination, these findings highlight the inconsistency between patient demand and the availability of evidence for BPIs targeting opioid use, related harm, and pain intensity. Future work should examine the effectiveness of BPIs in higher quality studies.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".