Anger differentially mediates the relationship between perceived injustice and chronic pain outcomes
Bibliographic record
Abstract
Emerging evidence suggests that perceived injustice is a risk factor for adverse outcomes associated with chronic pain. To date, however, the processes by which perceived injustice impacts on pain outcomes remain speculative. Evidence from several lines of research suggests that anger may mediate the relationship between injustice and pain outcomes. However, this relationship has not been empirically tested in patients with chronic pain. Thus, the purpose of this study was to examine whether anger mediates the relationships between perceived injustice and pain intensity, depressive symptoms, and self-reported disability. One hundred and seventy-three individuals with chronic musculoskeletal pain completed self-report measures of perceived injustice, anger, pain intensity, depressive symptoms, and disability. Consistent with previous research, high scores on a measure of perceived injustice were associated with greater pain, more severe depressive symptoms, and more pronounced disability. Hierarchical regression analyses indicated that anger variables completely mediated the relationship between perceived injustice and pain intensity, and partially mediated the relationship between perceived injustice and depressive symptoms. Anger did not mediate the relationship between perceived injustice and self-reported disability. The Discussion addresses the theoretical and clinical implications of the findings.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".