Everybody Hurts: Intersecting and Colliding Epidemics and the Need for Integrated Behavioral Treatment of Chronic Pain and Substance Use
Bibliographic record
Abstract
Chronic pain and substance use disorders are both common, debilitating, and often persist over the longer term. On their own, each represents a significant health problem, with estimates indicating a substantial proportion of the adult population has chronic pain or a substance use disorder (SUD), and their co-occurrence is increasing. Chronic pain and SUD are also both often invisible, stigmatized disorders and persons with both regularly have difficulty accessing evidence-based treatments, particularly those that offer coordinated and integrated treatment for both conditions. But there is hope. Research is unraveling the mechanisms of chronic pain and substance use, as well as their co-occurrence, integrated behavioral treatment options based on acceptance- and mindfulness-based approaches are increasingly being developed and tested, government agencies are devoting more funds and resources to increase research on chronic pain and SUD, and there have been growing efforts in training, dissemination, and implementation of evidence-based treatments. At the very heart of the matter, though, is to recognize that everybody hurts sometimes, and treatments must empower people to life effectively with these experiences of being human.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 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".