Reimagining Research with Pregnant Women and Parents Who Consume Cannabis in the Era of Legalization: The Value of Integrating Intersectional Feminist and Participatory Action Approaches
Bibliographic record
Abstract
Research on women who consume cannabis has predominantly focused on the perinatal period whereby cannabis consumption is problematized, linked to negative perinatal outcomes, and related to substance use and mental health challenges. When this historical literature and research about cannabis consumption is considered through a sociolegal and intersectional lens, questions emerge about how cannabis legalization may benefit and harm women, particularly women who experience marginalization along various axes of identity such as gender, race, and class. Questions also emerge about how women who consume cannabis may be perceived, represented, and treated as part of health and social care practices, particularly while pregnant and parenting. This commentary seeks to untangle what could be at stake for pregnant women and mothers, and what could be emphasized in future research endeavors, in the new era of cannabis legalization in Canada. The authors encourage research initiatives that attend to and reimagine harm reduction philosophies, and that integrate intersectional, feminist, and participatory action research approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.140 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.036 | 0.081 |
| Scholarly communication | 0.022 | 0.021 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".