Reprint of: Maternal adverse childhood experiences, child resilience factors, and child mental health problems: A multi-wave study
Bibliographic record
Abstract
BACKGROUND: Research suggests that maternal ACEs have intergenerational consequences for offspring mental health. However, very few studies have investigated moderators of this association. OBJECTIVES: The objective of this longitudinal study was to examine whether child resilience factors moderated the association between maternal ACEs prior to age 18, and child-reported symptoms of anxiety, depression, hyperactivity, and inattention. PARTICIPANTS AND SETTING: The current study used data from 910 mother-child dyads. Participants were recruited in pregnancy from 2008 to 2010 as part of a longitudinal cohort study. METHODS: Mothers had previously completed an ACEs questionnaire and reported on their child's resilience factors at child age 8-years. Children completed questionnaires about their mental health problems (symptoms of anxiety, depression, hyperactivity, and attention problems) at ages 10 and 10.5 years. Four moderation models were performed in total. RESULTS: Results revealed that maternal ACEs predicted child-reported symptoms of anxiety (β = 0.174, p = .02) and depression (β = 0.37, p = .004). However, both these associations were moderated by higher levels of perceived child resilience factors (β = -0.29, p = .02, β = -0.33, p = .008, respectively). Specifically, there was no association between maternal ACEs and child mental health problems in the context of moderate and high levels of child resilience factors. CONCLUSIONS: Children who have the ability to solicit support from internal and external sources (e.g., being creative, setting realistic goals, making friends easily) may be buffered against the consequences of maternal ACEs on anxiety and depression. Thus, the effects of maternal ACEs on child mental health problems are not deterministic.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".