Making Decisions about Cannabis Use during Pregnancy: A Qualitative Study
Bibliographic record
Abstract
Context: Since the Canadian legalization of recreational cannabis use in 2018, cannabis use has increased, including during pregnancy, although prevalence rates are difficult to determine. The evidence of clinical harm of prenatal cannabis use is still emerging, with some signals that prenatal cannabis use is associated with pre-term birth, small for gestational age, and some neurodevelopmental outcomes in childhood. However, many pregnant people perceive benefit to their cannabis use, and wish to continue using during pregnancy. Objective: Describe the motivations and decision-making processes of pregnant people who use cannabis. Study Design and Analysis: We used Constructivist Grounded Theory to conduct semi-structured interviews with pregnant or lactating people who made a decision about prenatal cannabis use. Setting or Dataset: All participants lived in Canada Population Studied: Pregnant or lactating people, 19 years of age or older who made a decision about ceasing, continuing, or initiating cannabis use. Intervention/Instrument: Semi-structured interviews Outcome Measures: Qualitative perceptions, experiences, opinions and beliefs. Results: We interviewed 52 pregnant and lactating people who spoke about their cannabis use decisions in their current and previous pregnancies. They perceived that cannabis use during pregnancy may carry risk to their fetus, although few were able to specifically articulate what that risk was. Those who perceived benefit to their cannabis use made deliberate and thoughtful decisions about how to balance that risk and benefit, using information gleaned online and from known sources. Those who continued to use cannabis during pregnancy were motivated by a desire to manage symptoms and secondarily, to cope with the unpleasant aspects of life. After giving birth, they were more likely to endorse recreational reasons for using cannabis. Conclusions: Participants considered cannabis use during pregnancy and lactation as two separate decisions. They were clear that their motivation to use cannabis was prompted by the benefits they perceived in relation to symptom management and coping with the unpleasant aspects of life. They made careful decisions, informed by the information that was available to them, although they often judged this information to be insufficient. They desire clear and comprehensive, evidence-based information to help them balance the risks and benefits of perinatal cannabis use.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".