确定美国年轻人使用机器学习的多年大麻电子烟的预测因素
Siyoung Choe1, Jon Agley1, Kit Elam1
1Department of Applied Health Science, Indiana University School of Public Health, 1025 E. 7th St., Bloomington, IN 47405-7109, USA.
Addictive behaviors
|September 28, 2024
概括
长期大麻电子烟的预测因素根据娱乐大麻合法化 (RCL) 状态而有所不同. 机器学习确定了RCL和没有RCL的州的年轻成年人多年vaping的独特因素.
科学领域:
- 公共卫生 公共卫生
- 大麻研究 大麻研究
- 青少年健康 青少年健康
背景情况:
- 年轻人越来越多地使用蒸发的大麻,但对预测因子的研究是有限的.
- 现有的研究往往忽略了复杂的相互作用和娱乐大麻合法化 (RCL) 的影响.
- 之前的研究集中在启动,而不是持续多年的大麻vaping模式.
研究的目的:
- 为了确定美国年轻成年人多年大麻vaping的预测因素.
- 在分析中考虑州级娱乐大麻合法化 (RCL) 状态.
- 利用先进的机器学习技术,以获得更全面的理解.
主要方法:
- 烟草和健康人口评估研究 (2016-2021) 的二次分析.
- 采用了两阶段的机器学习方法:LASSO和CART.
- 检查了多年大麻vaping的预测因素,考虑到州级RCL状态.
主要成果:
- 对于具有和没有RCL的状态,开发了不同的五终端节点预测模型.
- 在患有RCL的州,预测因素包括大麻使用,吸烟,欺凌和种族.
- 在没有RCL的州,预测因素包括大麻使用,海洛因使用,尼古丁vaping和水使用.
结论:
- 持续使用大麻电子烟的预测因子与开始使用大麻电子烟的预测因子不同.
- 娱乐大麻合法化 (RCL) 状态是影响大麻vaping预测因素的关键因素.
- 这些发现强调了在研究大麻蒸汽行为时需要考虑RCL状态的必要性.
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