Nicotine content, labelling and flavours of e-liquids in Canada in 2020: a scan of the online retail market
Bibliographic record
Abstract
<p dir="ltr">Introduction: The e-cigarette market in Canada has rapidly evolved following the implementation of the Tobacco and Vaping Products Act in May 2018, which liberalized the promotion and sale of vaping products. To date, there is little data on the market profile of key product attributes, including nicotine content, labelling practices and flavours. <p dir="ltr">Methods: An online scan of vaping product retailers (manufacturer, two national, five provincial) was conducted in 2020 to assess the e-liquids available on the Canadian market. Data were extracted from websites and product images regarding the nicotine content, labelling and flavours of e-liquids. <p dir="ltr">Results: We identified 1746 e-liquids, with a total of 4790 different nicotine concentrations. Approximately half of the e-liquids were offered with salt-base nicotine (46.6%) and half with freebase nicotine (53.2%); the remainder were hybrids (0.2%). The mean nicotine concentration of salt-base e-liquids (3.4%) was higher than freebase e-liquids (0.5%) (p < 0.001). Labels indicating the presence of nicotine were visible on twothirds of e-liquid packaging displayed online (63.2%) while three-quarters of packaging displayed the nicotine concentration (73.7%), and more than half of packaging displayed health warnings (58.9%). A variety of flavours were also identified, with fruit being the most common (43.6%), followed by candy/desserts (27.6%) and non-alcoholic drinks (12.5%). <p dir="ltr">Conclusion: Findings demonstrate the diversity of the online e-cigarette market in Canada, including the availability of higher-concentration salt-base nicotine products. Flavour restrictions have the potential to dramatically reduce the number of e-liquid flavours on the market, while restricting nicotine concentrations to < 20 mg/mL will predominantly restrict salt-based e-liquids.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".