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A machine learning approach to identify drivers of e-cigarette dependence Nominated Principal Investigator Michael Chaiton Independent Scientist, Centre for Addiction and Mental Health [email protected] Knowledge User Peter Selby Centre for Addiction and Mental Health Co-investigators Susan J Bondy Adam G Cole Tara E Elton-Marshall Hayley A Hamilton Sean Hill Scott Leatherdale Nikolaos Mitsakakis Robert M Schwartz Wei Wang Project Summary Understanding person-level drivers of current e-cigarette use (vaping) is crucial to guide tobacco policy, but prior studies have not fully identified these drivers due to the reliance on cross-sectional data, small sample sizes in many studies, lack of generalizability, and limitations of traditional data analyses

The couple has been officially married since March of 2020, when they put on all black and went to City Hall, but if you thought that was the end of their celebrations, youd have been wrong

Some of this is explained by changes in smoking behavior, known as compensatory smoking

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