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by Olav N. L. Aga, Sabrina J. Moyo, Joel Manyahi, Upendo Kibwana, Iren H. Löhr, Nina Langeland, Bjørn Blomberg, Iain G. Johnston Antimicrobial resistance (AMR) is a substantial and growing global health burden. Understanding, and predicting, its evolution in specific pathogens will help responses across scales from individual patient cases to large-scale policy. Here, we use global data on AMR features, predicted from 47k Klebsiella pneumoniae genomes, with hypercubic transition path sampling to infer the evolutionary pathways by which AMR features in K. pneumoniae (KpAMR) are acquired across 102 countries, territories, and areas. We identify “globally consistent” evolutionary behaviors that hold across countries, and “globally divergent” behaviors including carbapenem and fluoroquinolone resistance that vary across countries. We show how these divergent dynamics covary both with public health superregion and drug use policy, and reveal competing evolutionary pathways within and between countries. Using newly sequenced data across several decades from sub-Saharan Africa, we show that this inferred global roadmap of KpAMR evolution successfully predicts prospective evolutionary dynamics. Together, we hope that the ability to characterize and predict evolutionary dynamics of AMR acquisition, connected to socio-economic and drug policy predictors, will help strengthen our understanding of AMR evolution worldwide.

Psychosocial factors are associated with the well-being of university students, influencing their academic demands, degree of autonomy and control, and perceived support within their learning environment. Based on the demand–control–support model, the Questionnaire of Psychosocial Factors in University Environments (CFPAU, for its acronym in Spanish) was developed. This newly created instrument was designed to assess risk and protective factors in university students. This study included a total sample of 1221 Mexican students from two public universities in Mexico. The samples were randomly divided into two equivalent groups. Exploratory factor analysis (EFA) was performed on the first group (n1 = 611), and confirmatory factor analysis (CFA) was performed on the second group (n2 = 610) via the diagonally weighted least squares (DWLS) method. The final structure consisted of 5 global dimensions and 13 specific subscales, including psychological demands, active study and professional development opportunities, institutional quality and social relationships, recognition and career certainty, and school–life conflict. The CFA results showed adequate fit across the five dimensional models (CFI range = 0.921–1.00; TLI range = 0.910–1.00; RMSEA range = 0.000–0.065; SRMR range = 0.003–0.072). Factorial invariance by sex showed stability in the configural, metric, and scalar models, and subscale reliability was adequate (α and ω = 0.71–0.90). Furthermore, convergent and divergent validity were verified through correlations in the expected direction via the WHO-5 and DASS-21. These findings support the structural validity and internal consistency of the CFPAU, confirming its usefulness in identifying psychosocial risk and protective factors in university students.