Pregnant people’s perspectives on cannabis use during pregnancy: A systematic review and integrative mixed-methods research synthesis
Bibliographic record
Abstract
Background: Cannabis use during the perinatal period is rising. Objectives: To synthesize existing knowledge on the perspectives of pregnant people and their partners about cannabis use in pregnancy and lactation. Search strategy: We searched MEDLINE, APA PsycINFO, Cumulative Index to Nursing and Allied Health Literature, Social Science Citation Index, Social Work Abstracts, ProQuest Sociology Collection up until April 1, 2020. Selection criteria: Eligible studies were those of any methodology which included the perspectives and experiences of pregnant or lactating people and their partners on cannabis use during pregnancy or lactation, with no time or geographical limit. Data collection and analysis: We employed a convergent integrative approach to the analysis of findings from all studies, using Sandelowski’s technique of “qualitizing statements” to extract and summarize relevant findings from inductive analysis. Main results: We identified 23 studies of pregnant people’s views about cannabis use in pregnancy. Comparative analysis revealed that whether cannabis was studied alone or grouped with other substances resulted in significant diversity in descriptions of participant decision-making priorities and perceptions of risks and benefits. Studies combining cannabis with other substance seldom addressed perceived benefits or reasons for using cannabis. Conclusions: The way cannabis is grouped with other substances influences the design and results of research. A comparative analysis emphasizes the importance of understanding why a pregnant person might choose to use cannabis in order to foster dialogue about perceptions of benefit and strategies for risk mitigation.
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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 | 0.022 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".