The Association Between Perceived Injustice and Opioid Craving in Patients With Chronic Pain: The Mediating Role of Daily Pain Intensity, Negative Affect, and Catastrophizing
Bibliographic record
Abstract
Abstract: The rise in the use of opioids has been accompanied by increases in prescription opioid misuse among patients with chronic pain. Opioid craving has emerged as a robust determinant of opioid misuse, but the factors that contribute to opioid craving have yet to be fully elucidated. Perceived injustice has been linked to numerous negative pain outcomes, but little is known about its association with opioid craving. Objectives The first objective of this study was to examine the association between perceived injustice and opioid craving in patients with chronic pain who are prescribed opioids. We also examined whether pain intensity, negative affect, or catastrophizing mediated this association. Methods In this longitudinal diary study, patients (n = 103) completed a questionnaire assessing perceived injustice and then completed daily measures of pain intensity, negative affect, catastrophizing, and opioid craving for 14 consecutive days. Results A significant association was found between perceived injustice and opioid craving (P < 0.01), with higher levels of perceived injustice being associated with heightened levels of opioid craving. A 2-1-1 multilevel mediation analysis indicated that the association between perceived injustice and craving was significantly mediated by catastrophizing (P < 0.05). Conclusions The present study provides new insights into the factors that might contribute to opioid craving in patients with chronic pain. Our findings suggest that perceived injustice might lead to increased catastrophizing and, in turn, heightened daily levels of opioid craving. These findings could have implications for chronic pain management clinicians who prescribe opioids.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".