{"title":"Diversity-aware population modeling","authors":"","doi":"10.1038/s43588-025-00787-9","DOIUrl":null,"url":null,"abstract":"We propose a diversity-aware population modeling framework using Bayesian multilevel regression and post-stratification to quantify sociodemographic disparities in cognitive development. Our approach improved subgroup estimates, guiding targeted public health strategies and addressing biases in traditional models to support more equitable decision-making.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 3","pages":"194-195"},"PeriodicalIF":12.0000,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature computational science","FirstCategoryId":"1085","ListUrlMain":"https://www.nature.com/articles/s43588-025-00787-9","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
We propose a diversity-aware population modeling framework using Bayesian multilevel regression and post-stratification to quantify sociodemographic disparities in cognitive development. Our approach improved subgroup estimates, guiding targeted public health strategies and addressing biases in traditional models to support more equitable decision-making.