Counseling About Cannabis Use During Pregnancy and Lactation: A Qualitative Study of Patient and Clinician Perspectives
Bibliographic record
Abstract
INTRODUCTION: Legalization in many jurisdictions has increased the prevalence of cannabis use, including during pregnancy and lactation. Accordingly, clinicians providing perinatal and infant care are increasingly required to counsel about this topic, even if they do not feel comfortable or prepared for this conversation. The aim of this research was to explore how prenatal clinicians and pregnant and lactating women interact with cannabis consumption. METHODS: Using qualitative description, we conducted semi-structured interviews with 75 individuals in Canada: 23 clinicians who provide pregnancy and lactation care, and 52 individuals who made cannabis consumption decisions during pregnancy and/or lactation. Data were analyzed using inductive content analysis. RESULTS: Three phases of the clinical encounter influenced decision-making about cannabis consumption: initiation of a discussion about cannabis, sense-making, and the outcome of the encounter. Patients and clinicians described similar ideals for a counseling encounter about cannabis consumption during pregnancy or lactation: open, patient-centered conversation grounded in an informed decision-making model to explore the benefits, risks, and alternatives to cannabis. While clinicians described these values as reflecting real clinical interactions, patients reported that in their experience, actual interactions did not live up to these ideals. CONCLUSION: Clinicians and pregnant and lactating people report desiring the same things from a counseling interaction about cannabis: sharing of information, identification of values, and facilitation of a decision. Both groups endorse an open, nonjudgemental counseling approach that explores the reasons why a patient is considering cannabis consumption and reflects these reasons against available evidence and alternatives known to be safe.
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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 | 0.025 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".