Opioid and Cannabis Use During Pregnancy and Breastfeeding in Relation to Sociodemographics and Mental Health Status: A Descriptive Study
Bibliographic record
Abstract
OBJECTIVE: This study of Canadian women estimates the prevalence of opioid and cannabis use during pregnancy and cannabis use during the breastfeeding period and explores the sociodemographic and mental health characteristics associated with use. METHODS: A total of 13 000 women who gave birth between January and June 2018 were invited to participate in the Survey on Maternal Health by Statistics Canada; 7111 women participated for a response rate of 54.7%. Participants were asked about their mental health, supports during pregnancy, and substance use. Multivariable logistic regression was used to describe the relationship between sociodemographic and mental health characteristics and substance use during pregnancy and while breastfeeding. RESULTS: The prevalence of self-reported opioid use during pregnancy was 1.4% (95% confidence interval [CI] 1.1%-1.8%). A higher proportion of women reported using cannabis during pregnancy and while breastfeeding, at 3.1% (95% CI 2.5%-3.6%) and 2.6% (95% CI 2.1%-3.1%), respectively. Younger age, not being in a relationship, lower level of education, and thoughts of self-harm were significantly associated with cannabis use during pregnancy. Lower level of education and thoughts of self-harm were also significantly associated with cannabis use while breastfeeding, as were symptoms of postpartum depression and/or generalized anxiety. Lower level of education and symptoms of postpartum depression and/or generalized anxiety were also significantly associated with opioid use during pregnancy. CONCLUSION: The results of this survey show relatively low levels of opioid and cannabis use during pregnancy and cannabis use while breastfeeding in Canada. Different sociodemographic and mental health characteristics are associated with the use of these substances, and public health interventions and policies should take into account these factors.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".