Factors mediating the impacts of child abuse and intimate partner violence on chronic pain: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Most research on the health impacts of intimate partner violence (IPV) and child abuse has been conducted in Western countries and may not be generalizable to women living in different contexts, such as Saudi Arabia. Chronic pain, a disabling health issue associated with experiences of both child abuse and IPV among women, negatively impacts women's well-being, quality of life, and level of functioning. Yet, the psychosocial mechanisms that explain how abuse relates to chronic pain are poorly understood. We developed and tested a theoretical model that explains how both IPV and child abuse are related to chronic pain. METHODS: We recruited a convenience sample of 299 Saudi women, who had experienced IPV in the past 12 months, from nine primary health care centers in Saudi Arabia between June and August 2015. Women completed a structured interview comprised of self-report measures of IPV, child abuse, PTSD, depressive symptoms, chronic pain, and social support. Using Structural equation modeling (SEM), we analyzed the proposed model twice with different mental health indicators as mediators: PTSD symptoms (Model 1) and depressive symptoms (Model 2). RESULTS: Both models were found to fit the data, accounting for 31.6% (Model 1) and 32.4% (Model 2) of the variance in chronic pain severity. In both models, mental health problems (PTSD and depressive symptoms) fully mediated the relationship between severity of IPV and child abuse and chronic pain severity. Perceived family support partially mediated the relationship between abuse severity and depressive symptoms. CONCLUSIONS: These results underscore the significance of considering lifetime abuse, women's mental health (depressive and PTSD symptoms) and their social resources in chronic pain management and treatment.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".