Pain Experiences and Their Relation to Opioid Misuse Risk and Emotion Dysregulation
Bibliographic record
Abstract
Pain is a complex, multidimensional experience but often is measured as a unidimensional experience. This study aimed to separately assess the sensory and affective components of pain and identify their relations to important pain-related outcomes, particularly in terms of opioid misuse risk and emotion dysregulation among patients with chronic pain receiving treatment in Appalachia. Two hundred and twelve patients presenting to a multidisciplinary pain center completed the Difficulties in Emotion Regulation Scale (DERS-18), Screener and Opioid Assessment for Patients with Pain—Revised (SOAPP-R), and short-form McGill Pain Questionnaire (SF-MPQ). The sensory experience of pain was unrelated to emotion dysregulation (r = 0.06, p = 0.57 ) and weakly related to opioid misuse risk (r = 0.182, p < 0.05 ). In contrast, the affective experience of pain was moderately related to emotion dysregulation (r = 0.217, p < 0.05 ) and strongly related to opioid misuse risk (r = 0.37, p < 0.01 ). In addition, emotion dysregulation predicted variance in opioid misuse risk above and beyond the affective and sensory experiences of pain ((b = 0.693, p < 0.001 ). The results suggest patients with a strong affective experience versus sensory experience of pain and challenges with emotion regulation may require a more comprehensive intervention to address these underlying components in order to reduce their risk of misusing opioid medications.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".