Does interpersonal violence mediate the association between adverse childhood experiences and internalizing symptoms among women?
Bibliographic record
Abstract
Adverse childhood experiences (ACEs) and violence against women are both urgent public health crises, especially in low- and middle-income countries. ACEs and interpersonal violence are both linked to the development of internalizing mental health outcomes. However, the role of interpersonal violence in the association between ACEs and internalizing symptoms among women in South Africa has yet to be established. Therefore, this study aimed to assess whether interpersonal violence mediated the association between ACEs and internalizing symptoms among women in Soweto, South Africa. Data from baseline and 18-month follow-up from a large, longitudinal cohort that participated in the Bukhali trial (N = 862) were used in the current study. Mediation analyses examined the associations between ACEs, interpersonal violence victimization (violence exposure, violence dose, and number of perpetrators), and internalizing symptoms (i.e., anxiety, depression). The moderating role of perpetrator identity in the relationship between violence dose and internalizing symptoms was examined using regression. Results indicated that violence exposure, violence dose, and the number of perpetrators significantly mediated the association between ACEs and internalizing symptoms. Additionally, violence from a relative or distant individual strengthened the association between violence dose and internalizing symptoms compared to violence from an intimate partner. Results from this study highlight that interpersonal violence is an important risk factor in the developmental cascade from ACEs to internalizing symptoms in women in Soweto.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".