Adverse childhood experiences and maternal self‐efficacy: Examining the mediating role of intimate partner violence and the moderating role of caste membership in rural India
Bibliographic record
Abstract
Maternal self-efficacy (MSE) is associated with healthy functioning in mothers and children globally. Maternal exposure to adverse childhood experiences (ACEs) and intimate partner violence (IPV) is known to negatively impact MSE in high-income countries; however, the association has not been examined in low-and-middle-income countries, such as India, which face socioeconomic risks including poverty, illiteracy, and discrimination based on caste membership. The present study examines the mediating role of IPV in the association between ACEs (specifically-emotional, physical, and sexual abuse, neglect, household dysfunction, and discrimination) and MSE and tests caste membership as a moderator. A community-based, cross-sectional survey was performed with 316 mothers with at least one child between 0 and 24 months in a rural area in the North Indian state of Uttar Pradesh. A structural equation framework was used to test the moderated-mediation model. Results from the moderated-mediation model indicate that greater ACEs exposure was associated with lower MSE and this association was mediated by IPV exposure for low-caste but not high-caste mothers, even after controlling for wealth and literacy. These findings add to existing evidence on ACEs exposure as a significant burden for rural Indian mothers, negatively impacting parenting outcomes such as MSE. The critical role of caste membership is also highlighted, providing implications for future research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".