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Electronic Cigarette Use among Irish Youth: A Cross Sectional Study of Prevalence and Associated Factors

2015· article· en· W364729166 on OpenAlexaboutno aff
Kate Babineau, Keishia Taylor, Luke Clancy

Bibliographic record

VenuePLoS ONE · 2015
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
FundersAn Roinn Sláinte
KeywordsMedicineSocioeconomic statusLogistic regressionTobacco useDemographyCross-sectional studyEnvironmental healthGeeCigarette smokingGeneralized estimating equationQuarter (Canadian coin)PopulationGeography

Abstract

fetched live from OpenAlex

PURPOSE: To examine prevalence of, and factors associated with, e-cigarette use among young people aged 16-17 in Ireland. METHODS: In 2014, a representative sample of 821 young people aged 16-17 recruited from secondary schools completed a pen and paper survey on e-cigarette use, tobacco use, and socio-demographic items. FINDINGS: A total of 23.8% of respondents had used e-cigarettes at least once. Dual trial of tobacco and e-cigarettes was common with 69.5% of regular smokers and 30.4% of 'ever' smokers having tried e-cigarettes and 10.6% of current smokers using e-cigarettes regularly. 4.2% of never smokers have tried e-cigarettes. Overall, current e-cigarette use (once a month or more) was low (3.2%). Binary logistic regression conducted through generalized estimating equations (GEE) determined that controlling for other variables, current tobacco use and 'ever' tobacco use predicted ever e-cigarette use. Gender and school-level socioeconomic status were also independent predictors of ever e-cigarette use. Gender stood as the only predictor of on-going e-cigarette use, with males being more likely to regularly use e-cigarettes at least once a month. CONCLUSIONS: E-cigarette use among 16-17 year olds in Ireland is of note, with nearly a quarter of students having tried them. Concurrent or experimental use of e-cigarettes and tobacco is more common than sole use, while a small number have tried e-cigarettes without having tried tobacco.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.133
GPT teacher head0.308
Teacher spread0.175 · 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

Citations67
Published2015
Admission routes1
Has abstractyes

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