Predictors of drug use during pregnancy: The relative effects of socioeconomic, demographic, and mental health risk factors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: With limited Canadian research on predictors of drug use during pregnancy, the primary objective was to assess the relative effects of socioeconomic, demographic, and mental health risk factors associated with drug use during pregnancy. Predictors of an Apgar score < 7 and fetal macrosomia were examined as secondary outcomes. METHODS: This retrospective cohort study consisted of 25,734 pregnant women from Southwestern Ontario. Data were prospectively obtained from perinatal and neonatal databases at a tertiary hospital in London, Ontario. Using a Geographic Information System, neighborhood-level socioeconomic variables were obtained by mapping maternal postal codes. Separate logistic regressions were computed for all outcome variables. RESULTS: The rates of alcohol, tobacco, and cannabis use during pregnancy were 1.9%, 16.2%, and 2.3%, respectively. The mean maternal age was 29.4±5.4 years. Maternal age was inversely associated with alcohol, tobacco and cannabis use, whereas lone-parent household, depression, and anxiety increased the odds of substance use. Depression was the top risk factor of all three substances. Compared to women who were not depressed during pregnancy, women who were depressed were 2.15 times more likely to use alcohol (95% CI: 1.60, 2.90), 1.70 times more likely to smoke tobacco (95% CI: 1.48, 1.95), and 2.56 times more likely to use cannabis (95% CI: 1.95, 3.35). Adverse birth outcomes were also associated with overweight and obesity, gestational diabetes and insulin-dependent diabetes. CONCLUSIONS: Maternal depression is the primary risk factor of drug use during pregnancy. Policy interventions that target at-risk women are important considerations to improve maternal mental health.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it