Contemporary social context and patterns of prenatal cannabis use in Canada following legalization: a secondary analysis of prospective cohort data
Bibliographic record
Abstract
Abstract Background The epidemiology of prenatal cannabis use in Canada following legalization remains unknown despite increasing evidence for associated health risks. Our study aimed to identify current risk factors for, and patterns of, prenatal cannabis use and second-hand cannabis exposure in Alberta. Methods We conducted a secondary analysis of prospective data from a 2019 study in Calgary AB, of 153 pregnant (<28 weeks gestation at enrollment), English-speaking Alberta residents. We conducted descriptive analyses of prenatal cannabis use patterns (timing, frequency, dose, modes and reasons for use) and logistic regression to identify risk factors for direct use and second-hand exposure. Results Odds of prenatal cannabis use were significantly higher among those who did not own their home (Odds Ratio (OR) 3.1; 95% CI:1.6-9.6), smoked tobacco prenatally (OR 3.3,95% CI:1.2-9.3) and used illicit substances in the past (OR 3.2; 95% CI:1.7-9.9), and lower for those consuming alcohol prenatally (OR 0.3, 0.12-0.89). Among the 90 (58%) participants who used cannabis prenatally, the majority used for medicinal reasons (96%), at least daily (67%), by smoking (88%), in all trimesters of pregnancy (66%). Although reported dose-per-use was commonly low, cumulative doses over pregnancy were high. Interpretation Our study finds marked differences in prenatal cannabis use risk factors, and patterns of more frequent use sustained throughout pregnancy with perceived medicinal indications than pre-legalization studies. Prenatal care providers should include cannabis explicitly in medication counselling. Further prospective studies are needed as the impacts of prenatal cannabis on maternal and infant health in Canada may currently be underestimated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| 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) | 0.002 | 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".