Cannabis Use During Pregnancy: Insights from Online Discourse and Socioeconomic Indicators Across the USA and Canada
Bibliographic record
Abstract
Cannabis use is on the rise, driven by relaxing legal regulations and declining perceptions of harm. This trend, coupled with the increasing reliance on social media for health-related information, has sparked interest in cannabis use during pregnancy (CanPreg). This study examines online discourse about CanPreg on Twitter, analyzing 53,183 unique tweets from 32,744 users in the USA and Canada between 2012 and 2021. We investigate the spatio-temporal distribution of CanPreg discussions, key topical contexts within these conversations, and their correlations with socioeconomic and health indicators. The analysis reveals regional differences, with a relatively higher interest in CanPreg discussions in Canada compared to the USA. The online discourse is primarily focused on research, alongside criticism, personal experiences, queries, news sharing, and advertisements. Additionally, correlations between CanPreg tweet activity, poverty rates, and mental health metrics suggest a connection between online discussions and real-world behaviors. This study highlights the role of social media in health communication and provides insights to inform targeted intervention strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".