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Record W4295766138 · doi:10.3390/ijerph191811570

An International Systematic Review of Prevalence, Risk, and Protective Factors Associated with Young People’s E-Cigarette Use

2022· review· en· W4295766138 on OpenAlexaboutno aff
Jinyung Kim, Serim Lee, JongSerl Chun

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2022
Typereview
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental healthMedicineSmoking prevalencePopularityIntervention (counseling)Young adultDemographyCigarette smokingSmoking epidemiologyGerontologyPsychologyPsychiatryPopulation

Abstract

fetched live from OpenAlex

While the prevalence of young people's conventional cigarette use has decreased in many countries, the use of e-cigarettes has risen. To effectively counteract the growing popularity of e-cigarettes among young people internationally, researchers should know the exact prevalence as well as the protective and risk factors associated with vaping. Based on five eligibility criteria, 53 articles were chosen and analyzed by general characteristics, prevalence, sample characteristics, gender difference, protective factors, and risk factors. In this study, the international pooled prevalence of young people's lifetime e-cigarette use was 15.3%, the current use was 7.7%, and dual use was 4.0%. While the highest lifetime, current, and dual prevalence were found in Sweden, Canada, and the United Kingdom, respectively, the lowest prevalence was found in Germany, followed by South Korea and Sweden. Some protective and risk factors include perceived cost and danger of vaping, parental monitoring, internal developmental assets, cigarette use, family and peer smoking, exposure to online advertisements, and the presence of nearby retail stores. Based on this review, researchers and practitioners can develop different intervention programs and strategies for young smokers.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.141
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.155
GPT teacher head0.432
Teacher spread0.277 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations73
Published2022
Admission routes1
Has abstractyes

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