Étude transversale sur l’utilisation des médias sociaux chez les consommateurs de cannabis au Québec
Bibliographic record
Abstract
BACKGROUND : Social media platforms have transformed society, being used by a majority of adults in Québec (Canada), half of which report spending too much time on them. Cannabis is one of the most widely consumed psychoactive substances, and its use has been on the rise since it was legalized in Canada in 2018. The use of social media and cannabis represent emerging public health concerns. This cross-sectional study aims to describe the social media use profile and identify factors associated with problematic social media use (PSMU) among recreational cannabis users in Quebec. METHODS : In 2024, 1406 participants were recruited online via stratified random sampling and completed a self-reported questionnaire, including validated instruments measuring PSMU, social media use, cannabis and psychoactive substance use, online fear of missing out, depression and anxiety. A binary logistic regression model was developed, with PSMU as the dependent variable, and included variables of interest such as age and sex. RESULTS : Prevalence of PSMU was 27.9%. Significant associations were noted between PSMU and age 18–20 years (aOR = 4.24, 95% CI 1.36–13.19), male sex (aOR = 2.01, 95% CI 1.36–2.99), risk for problematic cannabis use (aOR = 1.10, 95% CI 1.05– 1.14), online fear of missing out (aOR = 1.16, 95% CI 1.14–1.18), and depressive symptoms (aOR = 1.05, 95% CI 1.02–1.09). The association between PSMU and the use of social media platforms was positive for Telegram (aOR = 3.11, 95% CI 1.86–5.21), TikTok (aOR = 2.62, 95% CI 1.84–3.72), Twitter (aOR = 1.59, 95% CI 1.05–2.40) and Facebook Dating (aOR = 1.61, 95% CI 1.02–2.55), and inverse for Snapchat (aOR = 0.61, 95% CI 0.39–0.93) and Threads (aOR = 0.26, 95% CI 0.12–0.55). CONCLUSION: The prevalence of PSMU among cannabis users is higher than the estimates for the general population in Québec, although these groups are distinct. Our results indicate a differential association between PSMU and the use of specific social media platforms. The probability of presenting this issue varies depending on the sociodemographic profile, risk for problematic cannabis use and mental health profile. This study highlights the importance of considering social media as a heterogeneous entity in the context of PSMU. A digital harm-reduction strategy, targeting at-risk groups, and tailored to the specificity of each social media platform, could represent a promising avenue of research and intervention for public health organizations and stakeholders.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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; both teacher heads agree on what is shown here.
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".