Adverse Childhood Experiences are Associated with illicit drug use among pregnant women with middle to high socioeconomic status: Findings from the All Our Families Cohort
Bibliographic record
Abstract
Abstract Background: Adverse childhood experiences (ACEs) are associated with illicit drug use among pregnant women who are socioeconomically vulnerable. While it is assumed that the impact of ACEs on illicit drug use is reduced among pregnant women who are well educated and have higher socioeconomic status, this assumption has not been well tested in the literature. The objective of this study was to examine the impact of maternal ACEs on illicit drug use among pregnant women who are well-educated women, have middle to high household incomes, and seeking regular prenatal care. Findings can inform clinicians about potential associations between ACEs and drug use in pregnancy within a population that they are frequently in contact with. Methods: This study is a secondary analysis of a prospective cohort study that collected data from 1,680 women during and after pregnancy in Calgary, Canada between 2008-2011 using mailed surveys. Illicit drug use in pregnancy was self-reported by women at 34-36 weeks gestation. An established scale examined maternal ACEs before 18 years. Logistic regression models and 95% confidence intervals tested associations between maternal ACE scores and illicit drug use in pregnancy. Results: Overall, 3.3% of women in this predominantly married, well-educated, middle and upper middle income sample (mean age 31 years) reported illicit drug use in pregnancy. Women with 2-3 ACEs had more than a two-fold increase, and women with 4 or more ACEs had almost a four-fold increase in illicit drug use in pregnancy, relative to women with 0-1 ACEs after adjustment for confounders. Exposure to child abuse was more consistently associated with illicit drug use in pregnancy than exposure to household dysfunction in childhood. Conclusions: Findings combine with others to speak to the public health significance of maternal ACEs on substance use among expectant mothers across the socioeconomic spectrum; particularly child abuse. This information, can be used by women and the clinicians serving them, to better understand the role that ACEs could play in their decision to use illicit drugs 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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".