Transforming Possible Risk Into Certain Harm: A Critical Interpretive Synthesis of the Literature on Perinatal Cannabis Use
Bibliographic record
Abstract
Substance use in pregnancy has been a prominent public health concern for the last several decades. Since the legalization of cannabis in Canada and across several American states, cannabis use during pregnancy has gained considerable public health, scientific, and media attention. This critical interpretive synthesis explores how the problem of cannabis use in pregnancy is constructed in the scientific literature and illuminates clinical, social, and political responses this construction engenders. The state of empirical evidence regarding the impact of perinatal cannabis use is fraught; a number of studies, of variable quality, have found no associations between cannabis use and adverse neonatal outcomes, while others have found cannabis to be associated with low birthweight and prematurity among other risks. Despite the inconsistent nature of the evidence base, the literature is underpinned by two important assumptions: prenatal cannabis exposure is an asocial phenomenon that can be disentangled from the social determinants of health, and cannabis exposure has detrimental effects on fetal and neonatal health. These assumptions shape indicators of signal and noise in the data by influencing the significance ascribed to particular findings, producing patterns of data interpretation that ultimately transform evidence of potential harms into certain risks and creates the mirage of a cohesive, unambiguous evidence base. We argue that the way that cannabis use in pregnancy is framed as a scientific and public health problem in the literature contributes to the stigmatization of pregnant people who use substances. We caution that failure to consider the interplay between environment, resources and other social determinants of health may ultimately cause undue harm to families and foreclose opportunities for investments that may promote health and well-being.
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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 | 0.124 | 0.249 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.023 | 0.012 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.023 | 0.019 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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".