Physician Communication and Perceived Stigma in Prenatal Cannabis Use
Bibliographic record
Abstract
Cannabis has long been widely used throughout the prenatal period. However, motives for prenatal cannabis use (PCU) have not been comprehensively examined. Stigmatization has been identified as a barrier to therapeutic cannabis use and related physician communication. Such stigma may be particularly salient in the context of PCU. One hundred and three women who reported current or past pregnancy were recruited online. Participants completed a survey querying prenatal experiences, substance use, and attitudes toward PCU. PCU was reported by 35 (34%) respondents. Treating nausea and vomiting of pregnancy was the most frequently reported reason for PCU (89%), and 24 (69%) reported substituting cannabis for pharmaceutical drugs. Sixty-two percent of PCU participants and 31% of non-PCU participants indicated discomfort discussing PCU with their physician, and 74% of PCU participants and 27% of non-PCU participants indicated they would not disclose PCU to their physician if it occurred in future pregnancies. Our findings suggest that PCU may reflect primarily therapeutic motives of relieving symptoms of morning sickness, nausea, low appetite, pain, and substituting for other prescription medications. Respondents reported discomfort discussing PCU with physicians, which was more pronounced among respondents with lived experience of PCU. Findings suggest PCU might best be evaluated within a therapeutic framework and highlight the importance of efforts to enhance patient-caregiver communication regarding PCU.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".