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Record W3028822106 · doi:10.1093/ntr/ntaa095

The Association of E-cigarette Flavors With Satisfaction, Enjoyment, and Trying to Quit or Stay Abstinent From Smoking Among Regular Adult Vapers From Canada and the United States: Findings From the 2018 ITC Four Country Smoking and Vaping Survey

2020· article· en· W3028822106 on OpenAlexafffundabout
Shannon Gravely, K. Michael Cummings, David Hammond, Eric N. Lindblom, Danielle M. Smith, Nadia Martin, Ruth Loewen, Ron Borland, Andrew Hyland, Mary E. Thompson, Christian Boudreau, Karin A. Kasza, Janine Ouimet, Anne C K Quah, Richard J. O’Connor, Geoffrey T. Fong

Bibliographic record

VenueNicotine & Tobacco Research · 2020
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsOntario Institute for Cancer ResearchUniversity of Waterloo
FundersNational Health and Medical Research CouncilNational Cancer InstituteMedical Research CouncilCanadian Institutes of Health ResearchOntario Institute for Cancer Research
KeywordsMentholFlavorPsychologyQuit smokingSmoking cessationFood scienceMedicineChemistry

Abstract

fetched live from OpenAlex

AIMS: This study examined whether nontobacco flavors are more commonly used by vapers (e-cigarette users) compared with tobacco flavor, described which flavors are most popular, and tested whether flavors are associated with: vaping satisfaction relative to smoking, level of enjoyment with vaping, reasons for using e-cigarettes, and making an attempt to quit smoking by smokers. METHODS: This cross-sectional study included 1603 adults from Canada and the United States who vaped at least weekly, and were either current smokers (concurrent users) or former smokers (exclusive vapers). Respondents were categorized into one of seven flavors they used most in the last month: tobacco, tobacco-menthol, unflavored, or one of the nontobacco flavors: menthol/mint, fruit, candy, or "other" (eg, coffee). RESULTS: Vapers use a wide range of flavors, with 63.1% using a nontobacco flavor. The most common flavor categories were fruit (29.4%) and tobacco (28.7%), followed by mint/menthol (14.4%) and candy (13.5%). Vapers using candy (41.0%, p < .0001) or fruit flavors (26.0%, p = .01) found vaping more satisfying (compared with smoking) than vapers using tobacco flavor (15.5%) and rated vaping as very/extremely enjoyable (fruit: 50.9%; candy: 60.9%) than those using tobacco flavor (39.4%). Among concurrent users, those using fruit (74.6%, p = .04) or candy flavors (81.1%, p = .003) were more likely than tobacco flavor users (63.5%) to vape in order to quit smoking. Flavor category was not associated with the likelihood of a quit attempt (p = .46). Among exclusive vapers, tobacco and nontobacco flavors were popular; however, those using tobacco (99.0%) were more likely than those using candy (72.8%, p = .002) or unflavored (42.5%, p = .005) to vape in order to stay quit. CONCLUSIONS: A majority of regular vapers in Canada and the US use nontobacco flavors. Greater satisfaction and enjoyment with vaping are higher among fruit and candy flavor users. While it does not appear that certain flavors are associated with a greater propensity to attempt to quit smoking among concurrent users, nontobacco flavors are popular among former smokers who are exclusively vaping. Future research should determine the likely impact of flavor bans on those who are vaping to quit smoking or to stay quit. IMPLICATIONS: Recent concerns about the attractiveness of e-cigarette flavors among youth have resulted in flavor restrictions in some jurisdictions of the United States and Canada. However, little is known about the possible consequences for current and former smokers if they no longer have access to their preferred flavors. This study shows that a variety of nontobacco flavors, especially fruit, are popular among adult vapers, particularly among those who have quit smoking and are now exclusively vaping. Limiting access to flavors may therefore reduce the appeal of e-cigarettes among adults who are trying to quit smoking or stay quit.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.049
GPT teacher head0.297
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations69
Published2020
Admission routes3
Has abstractyes

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