Abuse-Related Injury and Symptoms of Posttraumatic Stress Disorder as Mechanisms of Chronic Pain in Survivors of Intimate Partner Violence
Bibliographic record
Abstract
OBJECTIVE: To examine the role of abuse-related injury and posttraumatic stress disorder (PTSD) symptom severity in mediating the effects of assaultive intimate partner violence (IPV) severity, psychological IPV severity, and child abuse severity on chronic pain severity in women survivors of IPV. METHODS: Using data collected from a community sample of 309 women survivors of IPV, structural equation modeling was used to test a theoretical model of the relationships among the key variables. RESULTS: The theoretical model accounted for almost 38% of the variance in chronic pain severity. PTSD symptom severity was a significant mediator of the relationships of both child abuse severity (beta = 0.13) and assaultive IPV severity (beta = 0.06) with chronic pain severity. Lifetime abuse-related injury was also a significant mediator of the relationships between both child abuse severity (beta = 0.05) and assaultive IPV severity (beta = 0.06) and chronic pain severity. Child abuse severity made the largest significant contribution to the model (beta = 0.35). Assaultive IPV severity had a significant indirect effect (beta = 0.12) on chronic pain severity while psychological IPV severity had a significant direct effect (beta = 0.20). CONCLUSIONS: Management of chronic pain in IPV survivors requires attention to symptoms of PTSD, abuse-related injury, and lifetime experiences of violence. Ensuring that acute pain from injury is adequately treated and followed over time may reduce the extent of chronic pain in abused women. The results also support the importance of routine assessment for IPV and child abuse.
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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 | 0.005 |
| 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 | 0.000 |
| 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".