Pain Acceptance Partially Mediates the Relationship Between Perceived Injustice and Pain Outcomes Over 3 Months
Bibliographic record
Abstract
OBJECTIVES: Perceived injustice is a maladaptive cognitive appraisal of pain or injury, characterized by attributions of blame, unfairness, severity of loss, and irreparability of loss. Research suggests that perceived injustice may negatively affect pain outcomes by inhibiting the development of pain-related acceptance. The current study aimed to extend cross-sectional research by testing whether pain acceptance mediates the effects of perceived injustice on pain-related outcomes longitudinally. MATERIALS AND METHODS: Data was analyzed from a prospective study to examine the potential mediating role of pain acceptance on recovery 3 months after an episode of low back pain. Using Mechanical Turk, we recruited participants who experienced an episode of back pain within the preceding 2 weeks, 343 of whom completed measures of perceived injustice, pain acceptance, pain ratings, and quality of life at each of 3 timepoints (recruitment, 1 mo later, and 3 mo later). Path analyses were conducted to examine pain acceptance at 1 month as a potential mediator of the relationship between perceived injustice at recruitment and pain intensity, disability, and depressive symptoms at 3 months. RESULTS: Results indicated that perceived injustice at recruitment was directly related to pain intensity, disability, and depressive symptoms 3 months later, and that pain acceptance partially mediated these relationships. DISCUSSION: Although these findings provide further support for pain acceptance as a buffer for the deleterious effects of perceived injustice, they also highlight that adjunctive mechanisms should be investigated to provide more comprehensive clinical insight.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".