Problematic social media use among recreational cannabis users in Québec: A Cross-Sectional study
Bibliographic record
Abstract
Background: Over the past decade, Canada has legalized recreational cannabis, and the rising popularity of social media has coincided with the emergence of problematic social media use (PSMU) as a potential behavioural addiction. This study aims to describe social media use and identify factors associated with PSMU among adult recreational cannabis users in Québec, Canada. Methods: This cross-sectional study includes 1406 participants who used both social media and cannabis. Data was collected using validated instruments measuring PSMU (BSMAS), online fear of missing out (On-FoMO), risk for problematic cannabis use (CAST), mental health variables (GAD-7, PHQ-8), and sociodemographic characteristics. A regression model was used to identify factors associated with PSMU. Results: Approximately 27.9 % of participants exhibited PSMU. Increased odds of PSMU were associated with a younger age (18-20 years), male sex at birth, and higher CAST, PHQ-8, and On-FoMO scores. Use of Telegram, TikTok, Twitter (X) and Facebook Dating was associated with increased odds of PSMU, whereas use of Snapchat and Threads was associated with reduced odds. Conclusions: This study is among the first to examine PSMU in adult cannabis users. Among them, prevalence of PSMU is higher than estimates for the general population. The odds of PSMU vary by social media platform. These findings suggest a need for targeted public health strategies that address social media and cannabis use.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".