{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T17:33:04Z","timestamp":1793727184398,"version":"4.6.1"},"reference-count":42,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T00:00:00Z","timestamp":1791590400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["WORK: A Journal of Prevention, Assessment & Rehabilitation"],"published-print":{"date-parts":[[2026,11]]},"abstract":"\n Background<\/jats:title>\n Increasing integration of artificial intelligence (AI) into clinical workflows necessitates assessing how future healthcare professionals adapt to these technologies.<\/jats:p>\n <\/jats:sec>\n \n Object\u0131ve<\/jats:title>\n This study aims to determine the artificial intelligence literacy and anxiety levels of students at a Vocational School of Health Services (VSHS) and to analyze the factors influencing these levels.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n This cross-sectional and descriptive-predictive study invited 800 associate degree healthcare students, resulting in a final sample of 545 participants (response rate: 68.12%). Instruments included a Sociodemographic Form, the AI Literacy Scale (AILS), and the AI Anxiety Scale (AIAS).<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n The mean age of participants was\n \n \n 20.41<\/mml:mn>\n \u00b1<\/mml:mo>\n 4.07<\/mml:mn>\n <\/mml:math>\n <\/jats:inline-formula>\n years. While\n \n \n 55.6<\/mml:mn>\n %<\/mml:mtext>\n <\/mml:math>\n <\/jats:inline-formula>\n of students reported partial knowledge of AI, only\n \n \n 44.4<\/mml:mn>\n %<\/mml:mtext>\n <\/mml:math>\n <\/jats:inline-formula>\n reported active use of AI-supported tools. In univariate analyses, gender (\n \n \n p<\/mml:mi>\n =<\/mml:mo>\n .005<\/mml:mn>\n ,<\/mml:mo>\n \n \u03b7<\/mml:mi>\n 2<\/mml:mn>\n <\/mml:msup>\n =<\/mml:mo>\n 0.015<\/mml:mn>\n <\/mml:math>\n <\/jats:inline-formula>\n ), prior AI knowledge (\n \n \n p<\/mml:mi>\n =<\/mml:mo>\n .002<\/mml:mn>\n <\/mml:math>\n <\/jats:inline-formula>\n ), and active AI application use (\n \n \n p<\/mml:mi>\n =<\/mml:mo>\n .007<\/mml:mn>\n <\/mml:math>\n <\/jats:inline-formula>\n ) showed statistically significant differences in anxiety levels. Total AI literacy was weakly negatively correlated with AI anxiety (\n \n \n r<\/mml:mi>\n =<\/mml:mo>\n \u2212<\/mml:mo>\n 0.20<\/mml:mn>\n ,<\/mml:mo>\n p<\/mml:mi>\n <<\/mml:mo>\n .001<\/mml:mn>\n <\/mml:math>\n <\/jats:inline-formula>\n ). Multivariable regression analysis confirmed that the established model significantly predicted anxiety\n \n \n (<\/mml:mo>\n \n F<\/mml:mi>\n \n 7<\/mml:mn>\n ,<\/mml:mo>\n 537<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msub>\n =<\/mml:mo>\n 10.387<\/mml:mn>\n ,<\/mml:mo>\n p<\/mml:mi>\n <<\/mml:mo>\n .001<\/mml:mn>\n <\/mml:math>\n <\/jats:inline-formula>\n ), explaining\n \n \n 11.9<\/mml:mn>\n %<\/mml:mtext>\n <\/mml:math>\n <\/jats:inline-formula>\n of the total variance (\n \n \n \n R<\/mml:mi>\n 2<\/mml:mn>\n <\/mml:msup>\n =<\/mml:mo>\n .119<\/mml:mn>\n <\/mml:math>\n <\/jats:inline-formula>\n , Adjusted\n \n \n \n R<\/mml:mi>\n 2<\/mml:mn>\n <\/mml:msup>\n =<\/mml:mo>\n .108<\/mml:mn>\n <\/mml:math>\n <\/jats:inline-formula>\n ). Within the multivariable model, cognitive literacy subdimensions\u2014Awareness (\n \n \n \u03b2<\/mml:mi>\n =<\/mml:mo>\n \u2212<\/mml:mo>\n .143<\/mml:mn>\n ,<\/mml:mo>\n p<\/mml:mi>\n =<\/mml:mo>\n .002<\/mml:mn>\n <\/mml:math>\n <\/jats:inline-formula>\n ), Evaluation (\n \n \n \u03b2<\/mml:mi>\n =<\/mml:mo>\n \u2212<\/mml:mo>\n .116<\/mml:mn>\n ,<\/mml:mo>\n p<\/mml:mi>\n =<\/mml:mo>\n .027<\/mml:mn>\n <\/mml:math>\n <\/jats:inline-formula>\n ), and Ethics (\n \n \n \u03b2<\/mml:mi>\n =<\/mml:mo>\n \u2212<\/mml:mo>\n .093<\/mml:mn>\n ,<\/mml:mo>\n p<\/mml:mi>\n =<\/mml:mo>\n .046<\/mml:mn>\n <\/mml:math>\n <\/jats:inline-formula>\n )\u2014and male gender (\n \n \n \u03b2<\/mml:mi>\n =<\/mml:mo>\n \u2212<\/mml:mo>\n .188<\/mml:mn>\n ,<\/mml:mo>\n p<\/mml:mi>\n <<\/mml:mo>\n .001<\/mml:mn>\n <\/mml:math>\n <\/jats:inline-formula>\n ) were associated with lower anxiety. Conversely, the operational AI Usage subdimension (\n \n \n \u03b2<\/mml:mi>\n =<\/mml:mo>\n .106<\/mml:mn>\n ,<\/mml:mo>\n p<\/mml:mi>\n =<\/mml:mo>\n .023<\/mml:mn>\n <\/mml:math>\n <\/jats:inline-formula>\n ) was associated with higher anxiety.\n <\/jats:p>\n <\/jats:sec>\n \n Conclus\u0131ons<\/jats:title>\n Cognitive AI literacy (awareness, evaluation, and ethics) serves as a protective buffer against technological anxiety, whereas practical execution introduces accountability-related stress. Vocational health curricula should incorporate structured AI simulations, socio-technical frameworks, and ethical evaluation to foster digital confidence.<\/jats:p>\n <\/jats:sec>","DOI":"10.1177\/10519815261491277","type":"journal-article","created":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T07:34:06Z","timestamp":1791617646000},"page":"863-872","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence adaptation in the future healthcare workforce: Evaluating literacy and anxiety levels"],"prefix":"10.1177","volume":"85","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3913-1078","authenticated-orcid":false,"given":"P\u0131nar","family":"Arpac\u0131","sequence":"first","affiliation":[{"name":"Manisa Celal Bayar University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,10,10]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3448250"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.36941\/ajis-2021-0077"},{"key":"e_1_3_3_4_2","first-page":"1","article-title":"Trust is the first algorithm: a socio-technical framework for generative AI integration in a high-stakes, resource-constrained context","author":"Aqtam I","year":"2026","unstructured":"Aqtam I, Shouli M. 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