Perceived Injustice Moderates the Relationship between Pain and Depressive Symptoms among Individuals with Persistent Musculoskeletal Pain
Bibliographic record
Abstract
BACKGROUND: Numerous investigations report that depressive symptoms frequently coexist with persistent pain. However, evidence suggests that symptoms of depression are not an inevitable consequence of pain. Diathesis-stress formulations suggest that psychological factors interact with the stress of pain to heighten the risk of depressive symptoms. Perceptions of injustice have recently emerged as a factor that may interact with the stress of pain to increase depressive symptoms. OBJECTIVES: The purpose of the present study was to examine whether perceived injustice moderates the relationship between pain and depressive symptoms. METHODS: A total of 107 individuals with persistent musculoskeletal pain completed self-report measures of pain severity, depressive symptoms, perceived injustice and catastrophizing. RESULTS: A hierarchical regression analysis revealed that the interaction between pain severity and perceived injustice uniquely contributed an additional 6% of the variance to the prediction of depressive symptoms, beyond the main effects of these variables. Post hoc probing indicated that pain was significantly related to depressive symptoms at high, but not low levels of perceived injustice. This finding remained statistically significant even when controlling for pain catastrophizing. CONCLUSIONS: The results suggest that perceived injustice augments the relationship between pain severity and depressive symptoms. The inclusion of techniques specifically targeting perceptions of injustice may enhance the effectiveness of interventions aimed at reducing symptoms of depression for individuals presenting with strong perceptions of injustice.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".