Talking with pregnant women exposed to cannabis use after cannabis legalization: Experiences of professionals working in Québec's social and healthcare system
Bibliographic record
Abstract
Background The rising prevalence of prenatal cannabis use in high-income countries presents a growing concern for the medical community. Despite guidelines outlining risks, healthcare and social service professionals often struggle to discuss cannabis use with pregnant women. This study examines how these interactions have evolved following the Cannabis Act in Québec, focusing on how professionals respond to and provide guidance for women who report cannabis use during pregnancy. Methods This is a qualitative study using semi-structured interviews. Purposeful sampling was employed to recruit 19 professionals, including physicians, nurses, psychologists, and social workers. Data was analyzed using King's (2012) Template analysis technique. Results We identified three themes: a) how professionals talk about cannabis, b) what they talk about, and c) what practices they follow. Two key processes—i) exploration and assessment, and ii) action—were identified. Professionals tailor interventions, including counseling, psycho-emotional management, harm reduction, and referrals, based on risk levels and willingness to change. We observed differences among professionals based on the programs in which they work. Conclusions This study highlights the complex interactions between health professionals and pregnant women who use cannabis. It discusses the importance of integrating harm reduction strategies with person-centered approaches to address cannabis use. While professionals balance the need for openness with concerns about fetal health, a knowledge gap persists. Strengthening educational initiatives and expanding addiction expertise could enhance support and intervention practices, bridging gaps left by current evidence and regulatory frameworks.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".