“Digging in”: stigma and surveillance in the lives of pregnant and breastfeeding mothers who consume cannabis
Bibliographic record
Abstract
Since the shift to legalizing recreational cannabis use in Canada in 2018, there has been increased attention on the consequences of cannabis use on women’s reproductive and maternal health, with particular attention to the impact of cannabis in utero and through breastfeeding. This has resulted in an intense focus on the behaviors of individuals who consume cannabis during the perinatal period, which raises questions about the impact this has on women and mothers who have historically been under the surveillance of the Canadian public health, health and social care, and legal systems. Grounded in an intersectional feminist framework that acknowledges how race, ability, class, and other social positions impact and differentiate women’s experience, this paper presents findings emerging from a participatory arts-based research approach called Photovoice with 23 mothers living throughout Canada. All participants consumed cannabis during pregnancy and breastfeeding and illustrated through photographs and individual and group discussion how their experiences of intersectional stigma and surveillance by health and social care providers resulted in barriers to accessing cannabis-related information and support. Implications arising from our inquiry suggest there is a dire need for public health, perinatal care, and social care responses that run counter to the current context where stigma and fear prevent parents from accessing cannabis information and support.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.029 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| 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".