Perceived organizational performance (POP) is an important factor that influences employees’ attitudes and behaviors such as retention and turnover, which in turn improve or impede organizational sustainability. The current study aims to identify interaction patterns of risk factors that differentiate public health and human services employees who perceived their agency performance as low. The 2018 Federal Employee Viewpoint Survey (FEVS), a nationally representative sample of U.S. federal government employees, was used for this study. The study included 43,029 federal employees (weighted n = 75,706) among 10 sub-agencies in the public health and human services sector. The machine-learning classification decision-tree modeling identified several tree-splitting variables and classified 33 subgroups of employees with 2 high-risk, 6 moderate-risk and 25 low-risk subgroups of POP. The important variables predicting POP included performance-oriented culture, organizational satisfaction, organizational procedural justice, task-oriented leadership, work security and safety, and employees’ commitment to their agency, and important variables interacted with one another in predicting risks of POP. Complex interaction patterns in high- and moderate-risk subgroups, the importance of a machine-learning approach to sustainable human resource management in industry 4.0, and the limitations and future research are discussed.

Adolescents are particularly vulnerable to tobacco initiation and escalation. Identifying factors associated with adolescent tobacco susceptibility and use can guide tobacco prevention efforts. Novel machine learning (ML) approaches efficiently identify interactive relations among factors of tobacco risks and identify high-risk subpopulations that may benefit from targeted prevention interventions. Nationally representative cross-sectional 2013–2017 Global Youth Tobacco Survey (GYTS) data from 97 countries (28 high-income and 69 low-and middle-income countries) from 342,481 adolescents aged 13–15 years (weighted N = 52,817,455) were analyzed using ML regression tree models, accounting for sampling weights. Predictors included demographics (sex, age), geography (region, country-income), and self-reported exposure to tobacco marketing, secondhand smoke, and tobacco control policies. 11.9% (95% CI 11.1%-12.6%) of tobacco-naïve adolescents were susceptible to tobacco use and 11.7% (11.0%-12.5%) of adolescents reported using any tobacco product (cigarettes, other smoked tobacco, smokeless tobacco) in the past 30 days. Regression tree models found that exposure or receptivity to tobacco industry promotions and secondhand smoke exposure predicted increased risks of susceptibility and use, while support for smoke-free air policies predicted decreased risks of tobacco susceptibility and use. Anti-tobacco school education and health warning messages on product packs predicted susceptibility or use, but their protective effects were not evident across all adolescent subgroups. Sex, region, and country-income moderated the effects of tobacco promotion and control factors on susceptibility or use, showing higher rates of susceptibility and use in males and high-income countries, Africa and the Americas (susceptibility), and Europe and Southeast Asia (use). Tobacco policy-related factors robustly predicted both tobacco susceptibility and use in global adolescents, and interacted with adolescent characteristics and other environments in complex ways that stratified adolescents based on their tobacco risk. These findings emphasize the importance of efficient ML modeling of interactions in tobacco risk prediction and suggest a role for targeted prevention strategies for high-risk adolescents.

Cannabis use among young adults in the U.S. has nearly doubled in recent years, driven by diverse methods of consumption and evolving cannabis legalization. Multi-modal cannabis use among young adults is an emerging public health issue that remains underexplored. This study examines the prevalence, patterns, and predictors of multi-modal cannabis use, defined as the use of two or more administration methods of cannabis use (e.g., smoking, vaping, edibles, dabbing, other forms) in the past month, among U.S. young adults aged 18–34 years. Data from the 2022–2023 Behavioral Risk Factor Surveillance System (BRFSS) across 23 states (n = 7635; weighted n = 7,482,134) show that 57% of young adults reporting current cannabis use engaged in multi-modal use, with dual- and triple-mode use being the most common. Factors associated with higher odds of multi-modal use include sexual minority status, poor physical health, frequent cannabis use, and co-use of electronic cigarettes and alcohol. Recreational cannabis legalization (RCL) is significantly linked to higher odds of multi-modal use. These findings underscore the interplay between individual risk factors and cannabis policy environments in shaping multi-modal cannabis use behaviors. To mitigate potential harms, targeted prevention strategies should prioritize young adults at risk for cannabis use, addressing both personal and policy-related factors influencing multi-modal cannabis use.