Adolescent exposure to cannabis marketing following recreational cannabis legalization in Canada: A pilot study using ecological momentary assessment
Bibliographic record
Abstract
OBJECTIVE: The goal of this pilot study was to assess the feasibility of a 9-day, smartphone-based ecological momentary assessment (EMA) protocol for tracking the frequency of Canadian adolescents' exposures to cannabis marketing, their reactions to such exposures, and the context in which exposures occur in the real-world and in real-time. METHOD: Participants were n = 18 adolescents between the ages of 14 and 18 years of age. They used an EMA application to capture and describe cannabis marketing exposures through photographs and brief questionnaires assessing marketing channel and context. Participants also rated their reactions to each exposure in real-time. RESULTS: 2.3) exposures per participant during the 9-day study. Exposures tended to occur in the afternoon (45.0%) or evening (37.5%), and while participants were at home (70%) and alone (52.5%). Most exposures occurred through promotion by public figures (27.5%) or explicitly marked internet ads (27.5%). CONCLUSION: This is the first study to demonstrate the feasibility and utility of EMA to capture adolescent exposures to cannabis marketing as it occurs in participants' natural environments. Our research offers an early look at the predictable wave of cannabis advertising targeting youth and a promising approach for studying its impacts in a post-legalization context, as well as a strategy for assessing policies, such as advertising restrictions, intending to mitigate the harms of early cannabis use among youth.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".