Using publicly available data to predict recreational cannabis legalization at the county-level
A machine learning approach
Bibliographic Data
| ID | 14907887 |
|---|---|
| Authors | Barrett Montgomery (0000-0003-3512-1710, RTI International, corresponding author), Xiaoran Tong (0000-0002-4648-3330, University of Kentucky), Olga Vsevolozhskaya (0000-0001-9376-3645, University of Kentucky), James C Anthony (0000-0001-7176-0929, Michigan State University) |
| Year | 2024 |
| Volume | 125 |
| Pages | 104340 |
| Publication date | 2024-03-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Drug Policy (JOURNAL) |
| Journal identifiers | ISSN: 0955-3959 • E-ISSN: 1873-4758 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.drugpo.2024.104340 |
| PMID | 38342052 |
| OpenAlex | W4391731516 |
| Language | EN |
| References cited | 17 |
Cannabis · Census · Environmental health · Geography · Logistic regression · Machine learning · Political science · Population · Psychiatry · Recreation · Recreational use · Cannabis and Cannabinoid Research · Computer Science · Demography · HIV, Drug Use, Sexual Risk · Medicine · Substance Abuse Treatment and Outcomes
Data Analysis Using Regression and Multilevel/Hierarchical Models
Association Between Recreational Marijuana Legalization in the United States and Changes in Marijuana Use and Cannabis Use Disorder From 2008 to 2016
Early evidence of the impact of cannabis legalization on cannabis use, cannabis use disorder, and the use of other substances
Community-level policy responses to state marijuana legalization in Washington State
Predictors of voter support for the legalization of recreational cannabis use and supply via a national referendum
County-Level Differences in Support for Recreational Cannabis on the Ballot
In the Weeds
| Highly cited | No |
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