Evaluating trends in recruitment challenges in vape shop research, e-cigarette product characteristics and use among shop customers from 2019 to 2023: A mixed-methods study
Bibliographic record
Abstract
INTRODUCTION: Brick-and-mortar vape shops specialize in the sale of e-cigarettes and remain a primary source for purchasing emerging e-cigarette products. New regulatory policies have been implemented at local-, state- and federal-level; the retail environment at vape shops and product preferences among vape shop customers shifted accordingly. METHODS: From 2019 to 2023, we collected anonymous interview data from vape shop customers (n=572) from 83 vape shops in Southern California. We aggregated the data by month and treated each month as the unit of analysis to document changes in recruitment efforts among the vape shops in relation to major policy implementations over 4 years. We also examined the systematic fluctuations and trends in customers' e-cigarette product preferences and nicotine content in these products. RESULTS: The monthly average shop-level consent rate was 52.9% (SD=8.7), with an overall decreasing trend over time. It was necessary for our data collection team to approach a greater number of vape shops to obtain consent with implementation of various state and federal tobacco regulations and following COVID-19. We observed an increase in the purchase of disposable products and nicotine concentrations in the products, while the average use frequency remained the same. CONCLUSIONS: Our findings demonstrated that user preferences, product characteristics and challenges in research involving vape shops are closely associated to changes in regulations. We documented a dramatic increase in nicotine concentration in products. Future policies restricting the amount of nicotine in tobacco products at the federal level are necessary to protect consumers from further nicotine addiction. This study provides documentation over time of the drastic increases in nicotine concentration among e-cigarette users as a result of the fluctuations in the product market. Regulating nicotine content in tobacco products could safeguard against further unsafe modifications in e-cigarettes and other types of tobacco products.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".