Bibliographic record
Abstract
BACKGROUND: Emerging research suggests that perceptions of injustice after musculoskeletal injury can have a significant impact on a number of pain-related outcomes. AIMS: The purpose of this paper is to review evidence linking perceptions of injustice to adverse pain outcomes. For the purposes of this paper, perceived injustice is defined as an appraisal cognition comprising elements of the severity of loss consequent to injury ("Most people don't understand how severe my condition is"), blame ("I am suffering because of someone else's negligence"), a sense of unfairness ("It all seems so unfair"), and irreparability of loss ("My life will never be the same"). RESULTS: Cross-sectional studies show that high scores on perceptions of injustice are correlated with pain catastrophizing, fear of movement, and depression. Prospective studies show that high scores on perceived injustice are a prognostic indicator of poor rehabilitation outcomes and prolonged work disability. Research shows that perceptions of injustice interfere not only with physical recovery after injury, but perceptions of injustice also impact negatively on recovery of the mental health problems that might arise subsequent to traumatic injury. Although research has yet to address the process by which perceptions of injustice impact on pain-related outcomes systematically; possible mechanisms include attentional disengagement difficulties, emotional distress, maladaptive coping, heightened displays of pain behavior, anger, and revenge motives. CONCLUSIONS: Perceived injustice appears to be associated with problematic health and mental health recovery trajectories after the onset of a pain condition. Future directions for research and treatment are addressed.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".