Reasons for cannabis use during pregnancy and lactation: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Cannabis use among pregnant and lactating people is increasing, despite clinical evidence showing that cannabis use may be associated with low birth weight and childhood developmental deficits. Our objective was to understand why pregnant and lactating people use cannabis and how these motivations change across perinatal stages. METHODS: Using qualitative, constructivist grounded theory methodology, we conducted telephone and virtual interviews with 52 individuals from across Canada. We selected participants using maximum variation and theoretical sampling. They were eligible if they had been pregnant or lactating within the past year and had decided to continue, cease or decrease their cannabis use during the perinatal period. RESULTS: We identified 3 categories of reasons that people use cannabis during pregnancy and lactation: sensation-seeking for fun and enjoyment; symptom management of chronic conditions and conditions related to pregnancy; and coping with the unpleasant, but nonpathologized, experiences of life. Before pregnancy, participants endorsed reasons for using cannabis in these 3 categories in similar proportions, with many offering multiple reasons for use. During pregnancy, reasons for use shifted primarily to symptom management. During lactation, reasons returned to resemble those expressed before pregnancy. INTERPRETATION: In this study, we showed that pregnant and lactating people use cannabis for many reasons, particularly for symptom management. Reasons for cannabis use changed across reproductive stages. The dynamic nature of the reasons for use across stages speaks to participant perception of benefits and risks, and perhaps a desire to cast cannabis use during pregnancy as therapeutic because of perceived stigma.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".