Availability, Price, and Packaging of Electronic Cigarettes and E-Liquids in Guatemala City Retailers
Bibliographic record
Abstract
Introduction: Electronic cigarettes (e-cigarettes) have the potential to normalize smoking and undermine tobacco control efforts. However, if well regulated, they also have a potential as smoking cessation aids. This study sought to determine the availability and types of e-cigarettes and e-liquids in Guatemala. We also assessed packaging characteristics and price. Methods: We surveyed a convenient sample of 39 Guatemala City retailers and purchased all e-cigarettes and e-liquids available. Duplicate samples (same brand, e-liquid type, flavor, nicotine content, or packaging) were purchased when prices were different between each other. Country of manufacture, flavor, expiration date, nicotine concentration, and price were recorded. We also documented package marketing strategies and warning labels. Results: We purchased 64 e-cigarettes (53 unique and 11 duplicates) and 57 e-liquids (52 unique and 5 duplicates), mostly found on mall retailers. Most e-cigarettes (42, 66%) were first generation, followed by second (18, 28%) and third generations (4, 6%). Price of e-cigarettes differed significantly by generation. Most e-cigarettes (31, 58%) and 24 (46%) e-liquids did not include warning labels. Nicotine content was reported in 21 (39%) e-cigarettes that included e-liquids and 41 (79%) e-liquids' packages. Conclusions: E-cigarettes and e-liquids are available among a variety of retailers in Guatemala City and the industry is taking advantage of the fact that they are not regulated (eg, health claims, minimum sales age, and taxation). Our findings support the need for further research on e-cigarettes and e-liquids in Guatemala. Implications: To the best of our knowledge, this is the first study describing e-cigarettes and e-liquids available in retailers in a low/middle-income country like Guatemala. E-cigarettes and e-liquids were found in a variety of types, flavors, and nicotine concentrations in Guatemalan retailers. Our findings support the need for further research on e-cigarettes and e-liquids in Guatemala.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".