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This study compared two screening strategies: a demographic-only model that requires no additional data collection versus a psychosocial model that incorporates job satisfaction and job stress assessments.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We analyzed data from employees across 85 countries or territories. Dataset 1 (psychosocial model,\n                      <jats:italic>n<\/jats:italic>\n                      = 22,136) included demographic, occupational, and psychosocial variables. Dataset 2 (demographic model,\n                      <jats:italic>n<\/jats:italic>\n                      = 23,219) used only readily available information. Work addiction risk was assessed using the International Work Addiction Scale. Five machine learning algorithms (Logistic Regression, Random Forest, XGBoost, Neural Networks, Ensemble Voting) were evaluated on both full and Elastic Net-selected feature sets using cross-validation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The psychosocial model demonstrated superior performance. The best-balanced model (Logistic\u00a0Regression with Elastic Net selection) achieved balanced accuracy of 0.72 \u00b1 0.01, AUC-ROC of 0.80 \u00b1 0.01, and Youden's\n                      <jats:italic>J<\/jats:italic>\n                      of 0.45 \u00b1 0.02, correctly identifying 72.7% of at-risk individuals while maintaining 72.2% specificity. The demographic model demonstrated moderate performance (Ensemble Voting: balanced accuracy 0.61 \u00b1 0.01, AUC-ROC 0.65 \u00b1 0.01), yet retained practical utility, detecting 57.0% of at-risk cases. High-sensitivity configurations achieved detection rates of 71%, but with elevated false-positive rates, presenting important trade-offs for screening design.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion and Conclusions<\/jats:title>\n                    <jats:p>While psychosocial variables provide superior prediction, demographic-only models offer a practical\u00a0screening alternative that can identify over half of at-risk individuals without an additional data-collection burden. This trade-off between predictive performance and implementation feasibility has important implications for organizational screening programs.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1556\/2006.2026.00060","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T11:44:04Z","timestamp":1782819844000},"page":"900-914","update-policy":"https:\/\/doi.org\/10.1556\/policypage.crossmark.crossref.2","source":"Crossref","is-referenced-by-count":0,"title":["Low-burden data-driven work addiction screening: Comparing demographic and psychosocial predictors using machine learning"],"prefix":"10.1556","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2406-9550","authenticated-orcid":true,"given":"Natalia A.","family":"Woropay-Hordziejewicz","sequence":"first","affiliation":[{"name":"Faculty of Psychology, University of Warsaw, Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9375-1433","authenticated-orcid":true,"given":"Edyta","family":"Charzy\u0144ska","sequence":"additional","affiliation":[{"name":"Institute of Psychology, Institute of Pedagogy, Faculty of Social Sciences, University of Silesia in\u00a0Katowice, Katowice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6849-8800","authenticated-orcid":true,"given":"Aleksandra","family":"Bu\u017aniak","sequence":"additional","affiliation":[{"name":"Institute of Psychology, Faculty of Social Sciences, University of Gda\u0144sk, Gda\u0144sk, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4245-046X","authenticated-orcid":true,"given":"Stanis\u0142aw K.","family":"Czerwi\u0144ski","sequence":"additional","affiliation":[{"name":"Institute of Psychology, Faculty of Social Sciences, University of Gda\u0144sk, Gda\u0144sk, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6838-4640","authenticated-orcid":true,"given":"Zuzanna","family":"Schneider","sequence":"additional","affiliation":[{"name":"Institute of Psychology, Faculty of Social Sciences, University of Silesia in Katowice, Katowice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5707-3882","authenticated-orcid":true,"given":"Pawe\u0142 A.","family":"Atroszko","sequence":"additional","affiliation":[{"name":"Institute of Psychology, Faculty of Social Sciences, University of Gda\u0144sk, Gda\u0144sk, Poland"},{"name":"Department of Psychological Sciences, FLAME University, Pune, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"425","reference":[{"key":"B1","series-title":"arXiv. 10.48550\/arXiv.1603.04467","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1603.04467","article-title":"TensorFlow: Large-scale machine learning on heterogeneous systems","author":"Abadi, M.","year":"2016","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., \u2026 Zheng, X. 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