Perceived injustice and pain-related outcomes in children with pain conditions: A systematic review
Bibliographic record
Abstract
OBJECTIVE: Research indicates that perceived injustice significantly influences pain-related outcomes and is associated with delayed recovery in adults. This systematic review examines the relationship between perceived injustice and pain-related outcomes in children with pain conditions. METHODS: A search of published studies in English in PubMed, PsychInfo, and Cochrane Database of Systematic Reviews from database inception through December 2022 were performed. The search criteria focused on studies that measured perceived injustice and pain-related outcomes in children with pain conditions. Out of 56 articles screened, 8 met the inclusion criteria, providing data on 1240 children with pain conditions. RESULTS: The average age of participants across all studies was 14.12 years (SD = 2.25), with 68.2% being female. There was strong evidence that higher perceived injustice is associated with worse pain intensity, functional disability, mental health outcomes, and emotional, social, and school functioning. CONCLUSION: The results of this study underscore how perceptions of injustice are associated various pain-related outcomes across different domains of children's lives. The findings highlight the need for screening and treatments targeting injustice appraisals in pediatric populations with pain conditions. The discussion addresses possible determinants and mechanisms of perceived injustice, along with implications for research and clinical practice.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".