pgph.0000060.s001.docx (400.81 kB)
S1 Fig -
journal contribution
posted on 2021-12-08, 18:24 authored by Nayoung Kim, Wei-Yin Loh, Danielle E. McCarthy(DOCX)
History
Usage metrics
Categories
Keywords
targeted prevention strategiestargeted prevention interventionsshowing higher ratespredictors included demographicspast 30 daysnovel machine learningnationally representative crossna 239machine learning modelshealth warning messages1 %- 120 %- 12identifying factors associatedxlink "> adolescentsstratified adolescents basedage ), geographytobacco school educationtobacco industry promotionsefficient ml modeling2017 padolescents reported usingtobacco control policiestobacco risk predictionadolescent tobacco susceptibilitycontrol factorstobacco riskglobal adolescentssusceptibility ),risk subpopulationsadolescent subgroupsadolescent characteristicstobacco susceptibilitytobacco riskstobacco promotiontobacco producttobacco policytobacco marketingtobacco initiationsmokeless tobaccosmoked tobaccoincome ),weighted nsoutheast asiasampling weightsreported exposureparticularly vulnerablemay benefitfindings emphasizeevident acrosscomplex ways97 countries69 low6 %)5 %)15 years
Licence
Exports
RefWorksRefWorks
BibTeXBibTeX
Ref. managerRef. manager
EndnoteEndnote
DataCiteDataCite
NLMNLM
DCDC