Hypothetical e-liquid flavor ban and opinions among vapeshop retailers in the Greater Los Angeles Area
Bibliographic record
Abstract
INTRODUCTION: Evaluating anticipated responses to flavor bans in the context of vape shops is needed to inform legislation and enforcement. This cross-sectional study examined vape shop retailers' opinions about the potential impacts of an e-liquid flavor ban on shop sales and customer behavior-change intentions. METHODS: From December 2019 to October 2020 we conducted structured interviews over the phone with 46 brick-and-mortar vape shop retailers in the Greater Los Angeles Area. RESULTS: Most participants were managers (43.5%), followed by owners (26.1%) and clerks (26.1%). More than half (52.2%) reported that sales would drop a lot if flavored e-liquids were banned in all vape shops. Controlling for store position, multivariable linear regression showed that opposition to a hypothetical ban on non-tobacco flavored e-liquids was associated with participants' opinions that customers would likely not purchase tobacco flavored e-liquids (b= -0.44, p<0.01), and would likely use combustible tobacco products (b=0.47, p<0.05). CONCLUSIONS: In this cross-sectional study, vape shop retailers in the Greater Los Angeles Area reported that if a ban on non-tobacco e-liquid flavors occurred, they would oppose strongly, and that a ban would have a negative impact on their shop (e.g. loss in sales) and customer behavior (e.g. would replace vaping with smoking combustible tobacco products). Implications for research and practice are discussed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".