{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T07:55:14Z","timestamp":1790582114552,"version":"4.1.0"},"reference-count":27,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T00:00:00Z","timestamp":1784505600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T00:00:00Z","timestamp":1784505600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Methods Psych Res"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background\/Aim<\/jats:title>\n                    <jats:p>Risky alcohol use is common among university students and negatively impacts physical and psychosocial health. Current screening instruments focus only on behavioral indicators, neglecting psychosocial factors. This study aimed to develop an AI\u2010supported, brief student\u2010focused tool for assessing alcohol\u2010related risk and to evaluate its preliminary reliability and validity among university students.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>A total of 599 university students participated in this cross\u2010sectional study. From a pool of 59 items covering behavioral, psychological, social, and academic questions related to alcohol use, a new risk model was created using 15 items selected with ChatGPT\u2010supported AI algorithms (SARAS\u201015). Risk scores ranged from 0 to 27, categorizing participants into low, medium, and high\u2010risk groups. The model's performance was evaluated using machine learning methods such as Random Forest, Logistic Regression, SVM, and KNN, along with cross\u2010validation and ROC analysis. We also analyzed its internal consistency and its correlations with screening instruments (AUDIT, RAPS4\u2010QF, CAGE).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In machine learning analyses, the logistic regression model achieved the highest performance (93.5% accuracy, F1\u2010score\u00a0=\u00a00.864, sensitivity\u00a0=\u00a00.826, specificity\u00a0=\u00a00.959). ROC analysis demonstrated excellent discrimination between low\u2010risk (AUC\u00a0=\u00a00.96) and high\u2010risk (AUC\u00a0=\u00a00.93) groups, with strong discrimination for the intermediate\u2010risk group (AUC\u00a0=\u00a00.88). The Random Forest model achieved an overall accuracy of 87%, successfully differentiating between the low\u2010risk group (F1 score\u00a0=\u00a00.91) and the high\u2010risk group (F1 score\u00a0=\u00a01.00). The new model's Cronbach's alpha was 0.811, with strong convergent validity correlations with screening instruments (AUDIT,\n                      <jats:italic>r<\/jats:italic>\n                      \u00a0=\u00a00.861; RAPS4\u2010QF,\n                      <jats:italic>r<\/jats:italic>\n                      \u00a0=\u00a00.793; CAGE,\n                      <jats:italic>r<\/jats:italic>\n                      \u00a0=\u00a00.631).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The developed artificial intelligence\u2010supported 15\u2010item new risk score model is valid and reliable in assessing risky alcohol use among university students. This scale demonstrates a high level of agreement with traditional tests and can accurately detect three risk levels. Due to its multi\u2010domain content structure, which addresses both behavioral and psychosocial effects, it serves as a complementary screening tool for early risk identification and intervention planning.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mpr.70096","type":"journal-article","created":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T09:19:13Z","timestamp":1784539153000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Alternative Approach for Detecting Problematic Alcohol Use: Developing the\n                    <i>Student Alcohol Risk Assessment Scale\u201015<\/i>\n                    Using AI\u2010Assisted Machine Learning"],"prefix":"10.1002","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5411-1618","authenticated-orcid":false,"given":"\u015e\u00fckr\u00fc Alperen","family":"Korkmaz","sequence":"first","affiliation":[{"name":"\u00c7anakkale Onsekiz Mart University 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T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gamze","family":"\u00c7an","sequence":"additional","affiliation":[{"name":"\u00c7anakkale Onsekiz Mart University Center for Combating Addiction Application and Research  \u00c7anakkale T\u00fcrkiye"},{"name":"Department of Public Health Faculty of Medicine \u00c7anakkale Onsekiz Mart University  \u00c7anakkale 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