Navigating cannabis use during pregnancy: life trajectories, relationships, and contextual influences
Bibliographic record
Abstract
BACKGROUND: In Canada, between 3 and 7 % of women have reported using cannabis during pregnancy despite public health recommendations. While fetal risks are well researched, the socio-economic, psychological, and environmental factors shaping cannabis use before and during pregnancy remain underexplored. METHOD: Drawing on semi-structured interviews with eighteen women who consumed cannabis while being pregnant in Québec (Canada), this interpretive study explores the meanings pregnant women attach to cannabis consumption throughout their lives and pregnancy, the factors conditioning their decisions, and the impact of their choices on their wellbeing. RESULTS: Women's decisions to reduce, cease, or continue cannabis use during pregnancy are shaped by their life trajectories, gendered experiences, and broader socio-environmental influences. Pregnancy can serve as a motivation to stop or reduce consumption, but for some, especially those with a deep connection to cannabis, pressure for abstinence can generate stress and anxiety. Moreover, decision-making regarding cannabis use is tied to gender norms and inequalities that shape the meanings pregnant persons attribute to their own use during pregnancy. CONCLUSION: This research highlights how the interplay of long-term social, relational, and environmental factors shapes cannabis use during pregnancy. It underscores the need for tailored, non-stigmatizing public health interventions that acknowledges this complexity, while also addressing stress, anxiety, and informational gaps. Providing harm reduction strategies and context-sensitive support systems can help ensuring that pregnant women receive compassionate, evidence-based care to navigate cannabis use during 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".