87 Is Canadian Federal Legislation Effective in Preventing Youth Access to Vaping Initiation Products? A secret shopper and online study
Bibliographic record
Abstract
Abstract Background Electronic cigarette use (“vaping”) is associated with negative health outcomes. Vaping among youth has recently risen to the highest levels recorded. Between 2017 and 2018, Canadians 16 to 19 years old who reported vaping over the past 30 days increased by 74%. This directly coincides with the Tobacco and Vaping Products Act (the “Act”) enactment, which lifted the ban on e-cigarette importation and sales without pre-approval while attempting to restrict youth access to e-cigarettes, and the entry of JUUL e-cigarette products into the Canadian market. Objectives The purpose of the study was to evaluate the Act’s effectiveness in protecting the health of youth by preventing their access to vaping products, using a secret shopper interventional design. Design/Methods Study subjects were vaping and convenience store employees and deliverers of vaping products ordered online. Confederate researchers (minors aged 15-16 and young adults aged 19-21) entered retail stores to buy JUUL Starter Kits. Three improper scenarios were used: (1) a minor(s); (2) a young adult with no or invalid identification; and (3) a young adult with valid identification clearly purchasing on behalf of an accompanying minor. The main outcome measured was vendor willingness to sell, recorded upon exit from stores. Five minors also ordered JUUL Starter Kits online from their homes. Frequencies were calculated for each variable and tests of association were completed using chi square analysis. Results In total, 42.5% of vendors (51/120) were willing to sell JUUL Starter Kits to the young adults and minors. Most vendors requested identification in all scenarios (97/120, 80.8%) but, of these, 28 vendors were willing to sell even though no or false identification was provided (28.9% of those who requested identification). Of those vendors who did not request identification (23/130, 19.2%), all were willing to sell. Where a young adult was clearly buying vaping products for a minor, vendors were willing to sell 63.2% of the time (24/38, p=0.016). In five online purchase attempts, 60% of deliverers did not meet the Act’s verification requirements. Conclusion Given that almost half of the vendors were willing to sell JUUL Starter Kits in improper circumstances, the Act does not adequately achieve its goal of protecting the health of youth. To prevent youth access to vaping products, the Act needs stricter enforcement and amendment to impose positive obligations on vendors to request identification, to prohibit sales to adults buying for minors, and to require manufacturers to disclose the product contained in delivered parcels.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".