Alcohol and substance use in pregnancy during the COVID-19 pandemic
Bibliographic record
Abstract
The impact of the COVID-19 pandemic on rates of alcohol and substance use raises significant concerns, as substances may be coping mechanisms for social isolation and/or disruptions to employment and the economy. Pregnant women are currently experiencing unusually high rates of anxiety and depression symptoms and may be especially affected. We analysed results from an ongoing study of pregnant individuals in Canada: Pregnancy during the COVID-19 Pandemic. Participants were asked about current substance during pregnancy, and concerns about the threat of COVID-19 to their baby’s life, decreased quality of prenatal care, and whether they felt more socially isolated, experienced financial difficulties, or lost their job. The percentage of participants reporting use during pregnancy was 6.9% for alcohol, 3.7% for cannabis, 3.5% for tobacco, and 0.2% for illicit drugs. Odds for cannabis use increased by 0.9% for each unit increase in concern about lower quality prenatal care and by 2.1% for each unit increase in financial difficulties. Odds for tobacco use increased by 220.0% for loss of employment. COVID-19 concerns did not significantly predict alcohol use. Comprehensive public health strategies, including access to perinatal, mental health, and financial supports, as well as education around the effects of prenatal exposures and the possible effects of COVID-19 on the mother and baby, are important to facilitate healthy coping mechanisms for mothers, reduce substance use in pregnancy, and mitigate poor perinatal and long-term neurodevelopmental outcomes for babies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".