E-liquid product labels: the good, the bad, and the ugly
Bibliographic record
Abstract
Background E-liquid products have exploded in Canada over the last number of years. Although current federal regulations apply to some aspects of these products (eg, Made in Canada designation, net quantity, French/English text), there are a number of other packaging elements that are not covered by existing regulations. The purpose of this research was to enumerate what choices are being made on e-liquid packaging by manufacturers, surveille compliance with existing regulations, and to inform governments about packaging elements that could benefit from strengthened regulations. Methods A sample of 90 e-liquid products were obtained from across 5 Canadian provinces, representing 49 unique manufacturers/distributors. This included 26 non-nicotine products and 64 nicotine products. All e-liquid containers were examined for type of information presented on product packaging including branding, promotional elements, claims, usage instructions, and warnings. Results Diverse labeling practices among manufacturers were observed. Of the 90 samples examined, 62 were highly legible, 21 were of moderate legibility and 7 were low. Only 62% of the products had English and French text, with English predominant. All but one sample included an ingredient list. However, 11% of samples listed quantity of nicotine as separate from the ingredient list. 19% listed nicotine quantity with a handwritten mark. 6% of sample had no text warning related to children (keep out of reach of children). 10% of samples did not include specific age-restrictive text. Only 72% of sample included a danger/caution image (skull, exclamation mark!). Conclusions Labeling practices among our sample of e-liquid products varied widely, with some manufacturers having strong practices, whereas others falling well short. Future labeling regulations that consider the main elements described in this study will have the potential to provide consumers of e-liquid products with pertinent information they need to make informed purchase decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".