Pathways from Maternal Adverse Childhood Experiences to Substance Use in Pregnancy: Findings from the All Our Families Cohort
Bibliographic record
Abstract
Background: Exposure to adverse childhood experiences (ACEs) is a risk factor for maternal substance use in pregnancy, however, mechanisms by which maternal ACEs may influence substance use in pregnancy have not been fully explored. The current study examines the association between maternal ACEs and substance use in pregnancy ( i.e. , alcohol, smoking, and drug use) and explores mediating pathways. Methods: A community sample of 1,994 women as part of the All Our Families Cohort were recruited in pregnancy in Calgary, Canada, between 2008 and 2011. Women provided retrospective reports of ACE exposure before age 18 as well as reports of demographic information, substance use ( i.e. , moderate-to-high alcohol use, any smoking, or any drug use), a previous history of substance use difficulties, and depressive symptoms during pregnancy. Path analyses were used to examine maternal income, education, depression, and previous substance use as mediating variables. Results: There were significant indirect associations between maternal ACEs and maternal substance use in pregnancy via maternal education ( β = 0.05, p < 0.001), previous substance use ( β = 0.01, p = 0.001), and depression ( β = 0.02, p = 0.02). The direct effect of maternal ACEs on maternal substance in pregnancy remained significant after accounting for the indirect effects ( β = 0.22, 95% CI = 0.15–0.29, p < 0.001). Conclusions: Exposure to adversity in childhood can lead to socioeconomic and mental health difficulties that increase risk for substance use in pregnancy. Addressing these difficulties before pregnancy may help to reduce the potential for substance use in pregnancy.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".