Regulation profiles of e-cigarettes in the United States: a critical review with qualitative synthesis
Bibliographic record
Abstract
BACKGROUND: Electronic cigarettes (e-cigarettes) have been steadily increasing in popularity since their introduction to US markets in 2007. Debates surrounding the proper regulatory mechanisms needed to mitigate potential harms associated with their use have focused on youth access, their potential for nicotine addiction, and the renormalization of a smoking culture. The objective of this study was to describe the enacted and planned regulations addressing this novel public health concern in the US. METHODS: We searched LexisNexis Academic under Federal Regulations and Registers, as well as State Administrative Codes and Registers. This same database was also used to find information about planned regulations in secondary sources. The search was restricted to US documents produced between January 1(st), 2004, and July 14(th), 2014. RESULTS: We found two planned regulations at the federal level, and 74 enacted and planned regulations in 44 states. We identified six state-based regulation types, including i) access, ii) usage, iii) marketing and advertisement, iv) packaging, v) taxation, and vi) licensure. These were further classified into 10 restriction subtypes: sales, sale to minors, use in indoor public places, use in limited venues, use by minors, licensure, marketing and advertising, packaging, and taxation. Most enacted restrictions aimed primarily to limit youth access, while few regulations enforced comprehensive restrictions on product use and availability. CONCLUSIONS: Current regulations targeting e-cigarettes in the US are varied in nature and scope. There is greater consensus surrounding youth protection (access by minors and/or use by minors, and/or use in limited venues), with little consensus on multi-level regulations, including comprehensive use bans in public spaces.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".