Impact of maternal adverse childhood experiences on child socioemotional function in rural Kenya: Mediating role of maternal mental health
Bibliographic record
Abstract
Mothers in low- and middle-income countries (LMIC) suffer heightened vulnerability for adverse childhood experiences (ACEs), which is exacerbated by the multitude of risk factors associated with poverty and may lead to increased risk of psychiatric disorder. The constellation of complex, co-occurring biological, environmental, social, economic and psychological risk factors are in turn transmitted to her child, conferring vulnerability for adverse development. This study examines the association between maternal intra- and extra-familial ACEs, maternal education and the mental health of her child, mediated by maternal mental health. Mother-child dyads (n = 121) in Machakos, Kenya were examined cross-sectionally using self-report measures of ACEs, maternal mental health and child internalizing and externalizing mental health problems. The four models proposed to examine the relationship between intra- and extra-familial maternal ACEs and child internalizing and externalizing problems demonstrated indirect pathways through maternal mental health. These effects were found to be conditional on levels of maternal education, which served as a protective factor at lower levels of maternal ACEs. These models demonstrate how the impact of ACEs persists across the lifespan resulting in a negative impact on maternal mental health and conferring further risk to subsequent generations. Elucidating the association between ACEs and subsequent intergenerational sequelae, especially in LMIC where risk is heightened, may improve targeted caregiver mental health programs for prevention and intervention.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".