Women’s Perceptions, Beliefs, and Decision-making Process of Consuming Cannabis During Pregnancy and Lactation: An Interpretive Description
Bibliographic record
Abstract
Background: In Canada, cannabis is the most frequently used substance after tobacco during pregnancy and lactation, and rates are increasing. Accumulating evidence suggests that cannabis exposure has a detrimental impact on neonatal and childhood development, underscoring the urgency of understanding pregnant women’s perspectives, knowledge, and beliefs regarding cannabis use during the perinatal period. Aim: To gain a deeper understanding of cannabis consumption during pregnancy and lactation and insight into how health care professionals can effectively address the increasing prevalence of cannabis use in pregnancy. Methods: 10 Albertan female-identifying women who self-reported cannabis consumption during pregnancy and lactation were interviewed, and data were analyzed using qualitative interpretive description methodology. Findings: Participants viewed cannabis as “natural” and “safe” and defined their consumption during pregnancy and lactation as medicinal. Information-seeking behaviors included developing social groups of trusted individuals and reliance on anecdotal stories. Discussion: The decision to consume cannabis during pregnancy and lactation is complex and influenced by multiple factors, including views that cannabis is a safe and effective medicinal option and mistrust of health care professionals. This decision remains highly stigmatized and viewed by society as problematic, which contrasts women’s belief that cannabis contributes to their overall health. Keywords: cannabis, pregnancy, lactation.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".