Mothers Marketing to Mothers: An Exploration of Cannabis Use and Constructions of Motherhood on Instagram and Blog Posts
Bibliographic record
Abstract
Background: Cannamom culture (CMC), an online movement for acceptance of cannabis use among mothers, has gained traction on social media. The urgency for exploration of CMC in a Canadian context was enhanced through legalization of cannabis in 2018, followed by legalization of cannabis edibles in 2019. Objectives: We sought to explore cannamoms’ social media and blog posts, and their representations of cannabis use and motherhood. Methods: This qualitative study utilized reflexive thematic analysis of Canadian cannamom blog posts (N = 30) and Instagram posts (N = 34). Analysis of these outlets was considered through the lenses of similar social media phenomena, such as influencer, wine mom, wellness, and mental health cultural movements. Results: The overarching theme identified was mothers marketing to mothers , with outlets advertising cannabis products and coaching. Three inter-related sub-themes were present within posts that facilitated this marketing: (1) a focus on increased normalization, attempted destigmatization, and legalization of cannabis due to its favorable health effects compared to other normalized substances; (2) responsible and personalized cannabis consumption to promote health and wellness ; and (3) cannabis use as a way to achieve motherhood ideals including thinness, productivity, mental wellness, and engaged parenting. Conclusions: CMC spaces online reproduce intensive motherhood narratives and position cannabis as an acceptable solution to motherhood struggles. The focus on wellness and personalization draws on wellness movements by constructing cannabis consumption as natural, responsible, and health promoting. These findings have implications for policy makers and healthcare providers regarding mothers’ understandings of cannabis use and health.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".