Associations between anger and chronic primary pain: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Anger is a negative emotion characterized by antagonism toward someone or something, is rooted in an appraisal or attribution of wrongdoing, and is accompanied by an action tendency to undo the wrongdoing. Anger is prevalent in individuals with chronic pain, especially those with chronic primary pain. The associations between anger and pain-related outcomes (e.g., pain intensity, disability) have been examined in previous studies. However, to our knowledge, no systematic review or meta-analysis has summarized the findings of anger-pain associations through a focus on chronic primary pain. Hence, we sought to summarize the findings on the associations of anger-related variables with pain and disability in individuals with chronic primary pain. METHODS: All studies reporting at least one association between anger-related variables and the two pain-related outcomes in individuals with chronic primary pain were eligible. We searched electronic databases using keywords relevant to anger and chronic primary pain. Multiple reviewers independently screened for study eligibility, data extraction, and methodological quality assessment. RESULTS: Thirty-eight studies were included in this systematic review, of which 20 provided data for meta-analyses (2,682 participants with chronic primary pain). Of the included studies, 68.4% had a medium methodological quality. Evidence showed mixed results in the qualitative synthesis. Most anger-related variables had significant positive pooled correlations with small to moderate effect sizes for pain and disability. CONCLUSIONS: Through a comprehensive search, we identified several key anger-related variables associated with pain-related outcomes. In particular, associations with perceived injustice were substantial.
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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 | 0.019 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".