A Systematic Review of the Association Between Perceived Injustice and Pain-Related Outcomes in Individuals with Musculoskeletal Pain
Bibliographic record
Abstract
OBJECTIVE: A growing body of literature shows that justice-related appraisals are significant determinants of pain-related outcomes and prolonged trajectories of recovery. We conducted a systematic review of the literature assessing the relationship between perceived injustice and pain-related outcomes in individuals with musculoskeletal pain. DESIGN AND PARTICIPANTS: A search of published studies in English in PubMed, PsychInfo, Embase, and Cochrane Database of Systematic Reviews from database inception through May 2019 was performed. Search terms included "perceived injustice," "injustice appraisals," "perceptions of injustice," and "pain" or "injury." RESULTS: Thirty-one studies met inclusion criteria. Data for a total of 5,969 patients with musculoskeletal pain were extracted. Twenty-three studies (71.9%) reported on individuals with persistent pain lasting over three months, and 17 studies (53.1%) reported on individuals with injury-related musculoskeletal pain. Significant associations were found between perceived injustice and pain intensity, disability and physical function, symptoms of depression and anxiety, post-traumatic stress disorder, quality of life and well-being, and quality of life and social functioning. CONCLUSIONS: This systematic review summarizes the current evidence for the association between perceived injustice and pain-related outcomes. There is strong evidence that perceived injustice is associated with pain intensity, disability-related variables, and mental health outcomes. Implications and directions for future research are discussed.
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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 | 0.011 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".