Perceived Injustice as a Determinant of the Severity of Post-traumatic Stress Symptoms Following Occupational Injury
Bibliographic record
Abstract
BACKGROUND: The present study assessed the role of perceived injustice in the experience and persistence of post-traumatic stress symptoms (PTSS) following work-related musculoskeletal injury. METHODS: The study sample consisted of 187 individuals who were absent from work as a result of a musculoskeletal injury. Participants completed measures of pain severity, perceived injustice, catastrophic thinking, post-traumatic stress symptoms, and disability on three occasions at three-week intervals. RESULTS: Consistent with previous research, correlational analyses revealed significant cross-sectional relations between pain and PTSS, and between perceived injustice and PTSS. Regression analysis on baseline data revealed that perceived injustice contributed significant variance to the prediction of PTSS, beyond the variance accounted for by pain severity and catastrophic thinking. Sequential analyses provided support for a bi-directional relation between perceived injustice and PTSS. Cross-lagged regression analyses showed that early changes in perceived injustice predicted later changes in PTSS and early changes in PTSS predicted later changes in perceived injustice. CONCLUSIONS: Possible linkages between perceived injustice and PTSS are discussed. The development of effective intervention techniques for targeting perceptions of injustice might be important for promoting recovery of PTSS consequent to musculoskeletal injury.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".