Cannabis advertising impacts on youth cannabis use intentions following recreational legalization in Canada: An Ecological Momentary Assessment (EMA) study
Bibliographic record
Abstract
OBJECTIVE: In 2018, Canada's Cannabis Act legalized adult recreational cannabis use and limited cannabis product advertising to adults. Cannabis product advertising to youth remains illegal. The extent to which adult-targeted, or illicit youth-targeted cannabis advertisements is reaching and impacting Canadian youth is unknown. We used Ecological Momentary Assessment (EMA) to describe how often and how much exposures to cannabis advertising influence Canadian youths' real-world, real-time intentions to use cannabis. METHODS: 120 Ontario, Canada youths ages 14-18, took photos of cannabis advertising that they encountered in their natural environments over a period of nine consecutive days. Following each exposure and twice daily device-issued random prompts, they also rated their intentions to use cannabis. RESULTS: Many participating youth (n = 85; 70.83 %) reported at least one cannabis advertising exposure during the study (range 1-30, M = 4.02). Exposures occurred through a range of advertising channels (e.g., internet ads, billboards). Multilevel modeling showed that youth advertising exposure increased cannabis use intentions in vivo (β = 0.06,SE = 0.03;t = 1.98;p =.04;n = 1,348). CONCLUSION: Data from this study shows that cannabis advertisements are regularly reaching Canadian youth and increasing their intentions to use cannabis. This suggests that current Canadian prohibitions on cannabis advertising to youth are ineffective and/or ineffectively enforced, and that the Canadian government needs additional or enhanced prohibitions on cannabis promotion to protect youth from harms associated with increased advertisement exposure, such as increased 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".