{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T18:06:10Z","timestamp":1785434770488,"version":"3.56.0"},"reference-count":29,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T00:00:00Z","timestamp":1785369600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100019004","name":"Deanship of Research, Jordan University of Science and Technology","doi-asserted-by":"publisher","award":["20240250"],"award-info":[{"award-number":["20240250"]}],"id":[{"id":"10.13039\/501100019004","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"\n Background<\/jats:title>\n Artificial Intelligence (AI) has emerged as a transformative force revolutionizing various sectors, including healthcare, particularly the mental health field. However, the acceptance and integration of AI technologies in different healthcare systems can be influenced by various factors, including cultural, social, and individual aspects. Nevertheless, there is a need for a valid and reliable tool for assessing AI\u2019s perception among healthcare consumers.<\/jats:p>\n <\/jats:sec>\n \n Aim<\/jats:title>\n To develop and validate a tool for the perception of AI among healthcare consumers and apply the tool to assess AI\u2019s perception among mental health consumers in the Jordanian healthcare system.<\/jats:p>\n <\/jats:sec>\n \n Method<\/jats:title>\n A cross\u2010sectional descriptive correlational design was utilized in the study. Data was collected from a convenience sample of 431 mental health consumers visiting mental health clinics of the International Medical Corps and university hospitals in Jordan. Structured interviews were conducted using an AI Perception (AIP) questionnaire developed by the authors. The questionnaire\u2019s content validity was assessed by an expert panel. Using Principal Component Analysis (PCA), the construct validity of the tool was evaluated, and its internal consistency was examined using Cronbach\u2019s alpha. Descriptive statistics were used to assess the levels of AI perception among participants.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n The final AIP tool consisted of 20 items across 4 domains and has demonstrated strong internal consistency across its four domains: AI acceptance and readiness (\u03b1\u2009=\u20090.92), AI perceived importance (\u03b1\u2009=\u20090.92), AI perceived risk (\u03b1\u2009=\u20090.9), and AI perceived challenges (\u03b1\u2009=\u20090.85). The construct validity of the four-domain structure of the tool was supported by PCA. Additionally, the mean scores for each domain indicated the average level of agreement with AI perception items among participants. Specifically, the mean score for AI acceptance and readiness was\n \n \n \n \n \n (<\/mml:mo>\n \n 2<\/mml:mtext>\n <\/mml:mrow>\n .<\/mml:mo>\n \n 7<\/mml:mtext>\n <\/mml:mrow>\n \u00a0<\/mml:mtext>\n \u00b1<\/mml:mo>\n \u00a0<\/mml:mtext>\n \n 0<\/mml:mtext>\n <\/mml:mrow>\n .<\/mml:mo>\n 96<\/mml:mtext>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n ). AI perceived importance was (2.18\n \n \n \n \n \n \u00b1<\/mml:mo>\n \u00a0<\/mml:mtext>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n 0.83), AI perceived risk was (2.58\n \n \n \n \n \n \u00b1<\/mml:mo>\n \u00a0<\/mml:mtext>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n 0.92), and AI perceived challenge was (2.78\n \n \n \n \n \n \u00b1<\/mml:mo>\n \u00a0<\/mml:mtext>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n 0.87).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n The findings of this study resulted in developing a valid and reliable 20-item tool to assess AI\u2019s perception among mental health consumers. The tool can be used to assess the predictors of AI\u2019s readiness among mental health consumers. Therefore, aiding policymakers and other stakeholders in understanding the AI adoption barriers from the perspective of end-users. In addition, this study developed the AIP tool that can be validated and used among other populations in future research.<\/jats:p>\n <\/jats:sec>","DOI":"10.1371\/journal.pone.0354305","type":"journal-article","created":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T17:27:41Z","timestamp":1785432461000},"page":"e0354305","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Development of a scale for measuring the perception of artificial intelligence among mental health consumers"],"prefix":"10.1371","volume":"21","author":[{"given":"Reema","family":"Al-Daraiseh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4381-8772","authenticated-orcid":true,"given":"Wafa\u2019a","family":"Ta\u2019an","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0200-9845","authenticated-orcid":true,"given":"Tareq","family":"Mukattash","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed M.","family":"Al-Hammouri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rana","family":"Abu-Farha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6307-1779","authenticated-orcid":true,"given":"Brett","family":"Williams","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,7,30]]},"reference":[{"key":"pone.0354305.ref001","doi-asserted-by":"crossref","DOI":"10.17226\/27111","volume-title":"Artificial intelligence in health care: The hope, the hype, the promise, the peril","author":"M Matheny","year":"2019"},{"issue":"2","key":"pone.0354305.ref002","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1177\/2472630319890316","article-title":"CURATE.AI: optimizing personalized medicine with artificial intelligence","volume":"25","author":"A Blasiak","year":"2020","journal-title":"SLAS Technol"},{"issue":"2","key":"pone.0354305.ref003","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1080\/09638237.2020.1714011","article-title":"\u201cAI gone mental\u201d: engagement and ethics in data-driven technology for mental health","volume":"29","author":"S Carr","year":"2020","journal-title":"J Ment Health"},{"issue":"4","key":"pone.0354305.ref004","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2021.e06626","article-title":"Applications of artificial intelligence to improve patient flow on mental health inpatient units - Narrative literature review","volume":"7","author":"P Cecula","year":"2021","journal-title":"Heliyon"},{"issue":"3","key":"pone.0354305.ref005","doi-asserted-by":"crossref","first-page":"279","DOI":"10.3325\/cmj.2020.61.279","article-title":"Artificial intelligence in prediction of mental health disorders induced by the COVID-19 pandemic among health care workers","volume":"61","author":"K \u0106osi\u0107","year":"2020","journal-title":"Croat Med J"},{"issue":"1","key":"pone.0354305.ref006","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1111\/cts.12884","article-title":"Precision medicine, AI, and the future of personalized health care","volume":"14","author":"KB Johnson","year":"2021","journal-title":"Clinical and Translational Science"},{"key":"pone.0354305.ref007","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/B978-0-12-818438-7.00002-2","article-title":"The rise of artificial intelligence in healthcare applications","volume-title":"Artificial Intelligence in Healthcare","author":"A Bohr","year":"2020"},{"issue":"12","key":"pone.0354305.ref008","doi-asserted-by":"crossref","DOI":"10.2196\/24578","article-title":"Flip the Clinic: A Digital Health Approach to Youth Mental Health Service Delivery During the COVID-19 Pandemic and Beyond","volume":"7","author":"TA Davenport","year":"2020","journal-title":"JMIR Ment Health"},{"issue":"4","key":"pone.0354305.ref009","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1136\/svn-2017-000101","article-title":"Artificial intelligence in healthcare: past, present and future","volume":"2","author":"F Jiang","year":"2017","journal-title":"Stroke Vasc Neurol"},{"key":"pone.0354305.ref010","doi-asserted-by":"crossref","first-page":"102443","DOI":"10.1016\/j.techsoc.2023.102443","article-title":"Artificial intelligence in healthcare institutions: A systematic literature review on influencing factors","volume":"76","author":"JS Roppelt","year":"2024","journal-title":"Technology in Society"},{"issue":"1","key":"pone.0354305.ref011","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1038\/s41746-019-0132-y","article-title":"Patients\u2019 views of wearable devices and AI in healthcare: findings from the ComPaRe e-cohort","volume":"2","author":"VT Tran","year":"2019","journal-title":"NPJ Digital Medicine"},{"key":"pone.0354305.ref012","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.ijhm.2019.01.005","article-title":"Developing and validating a service robot integration willingness scale","volume":"80","author":"L Lu","year":"2019","journal-title":"International Journal of Hospitality Management"},{"key":"pone.0354305.ref013","doi-asserted-by":"crossref","first-page":"149","DOI":"10.3389\/fpubh.2018.00149","article-title":"Best practices for developing and validating scales for health, social, and behavioral research: a primer","volume":"6","author":"GO Boateng","year":"2018","journal-title":"Frontiers in Public Health"},{"key":"pone.0354305.ref014","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-642-69746-3_2","article-title":"From intentions to actions: A theory of planned behavior.","volume-title":"Action control: From cognition to behavior","author":"I. Ajzen","year":"1985"},{"issue":"3","key":"pone.0354305.ref015","doi-asserted-by":"crossref","first-page":"319","DOI":"10.2307\/249008","article-title":"Perceived usefulness, perceived ease of use, and user acceptance of information technology","volume":"13","author":"FD Davis","year":"1989","journal-title":"MIS Quarterly"},{"issue":"000003","key":"pone.0354305.ref016","first-page":"111","article-title":"Risk taking and information handling in consumer behavior","volume":"32","author":"JF Engel","year":"1968","journal-title":"Journal of Marketing"},{"key":"pone.0354305.ref017","article-title":"Consumer behavior as risk-taking.","volume-title":"Dynamic marketing for a changing world","author":"RA. Bauer","year":"1960"},{"key":"pone.0354305.ref018","doi-asserted-by":"crossref","unstructured":"Fleischmann C, Cardon PW, Aritz J. Smart collaboration in global virtual teams: The influence of culture on technology acceptance and communication effectiveness. In: HICSS. 2020;1\u201311.","DOI":"10.24251\/HICSS.2020.045"},{"issue":"2","key":"pone.0354305.ref019","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1002\/nur.20247","article-title":"Considerations in determining sample size for pilot studies","volume":"31","author":"MA Hertzog","year":"2008","journal-title":"Research in Nursing & Health"},{"issue":"1","key":"pone.0354305.ref020","doi-asserted-by":"crossref","DOI":"10.5395\/rde.2024.49.e3","article-title":"Sample size determination for conducting a pilot study to assess reliability of a questionnaire","volume":"49","author":"MA Bujang","year":"2024","journal-title":"Restorative Dentistry & Endodontics"},{"key":"pone.0354305.ref021","volume-title":"Psychometric theory","author":"JC Nunnally","year":"1978","edition":"2"},{"issue":"10","key":"pone.0354305.ref022","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1002\/j.1477-8696.1990.tb05558.x","article-title":"Principal component analysis: a beginner\u2019s guide \u2014 I. Introduction and application","volume":"45","author":"IT Jolliffe","year":"1990","journal-title":"Weather"},{"key":"pone.0354305.ref023","first-page":"196","article-title":"Social determinant of mental health in immigrants and refugees: a systematic review","volume":"35","author":"A Rashki Kemmak","year":"2021","journal-title":"Med J Islam Repub Iran"},{"issue":"6","key":"pone.0354305.ref024","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1080\/15313204.2020.1753614","article-title":"Employment of Syrian refugees in Jordan: challenges and opportunities","volume":"30","author":"Z \u015eahin Menc\u00fctek","year":"2020","journal-title":"J Ethnic Cultural Diversity in Social Work"},{"issue":"2","key":"pone.0354305.ref025","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1080\/10447318.2022.2151730","article-title":"The Roles of Personality Traits, AI Anxiety, and Demographic Factors in Attitudes toward Artificial Intelligence","volume":"40","author":"F Kaya","year":"2022","journal-title":"International Journal of Human\u2013Computer Interaction"},{"key":"pone.0354305.ref026","doi-asserted-by":"crossref","unstructured":"Grassini S, Ree AS. Hope or Doom AI-ttitude? Examining the Impact of Gender, Age, and Cultural Differences on the Envisioned Future Impact of Artificial Intelligence on Humankind. In: Proceedings of the European Conference on Cognitive Ergonomics 2023,. 2023. 1\u20137. https:\/\/doi.org\/10.1145\/3605655.3605669","DOI":"10.1145\/3605655.3605669"},{"key":"pone.0354305.ref027","unstructured":"D\u2019Souza F. Awareness and adoption of AI technologies in the libraries of Karnataka. arXiv preprint. 2024. https:\/\/doi.org\/arXiv:2407.18933"},{"key":"pone.0354305.ref028","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compedu.2016.11.003","article-title":"Gender and attitudes toward technology use: A meta-analysis","volume":"105","author":"Z Cai","year":"2017","journal-title":"Computers & Education"},{"key":"pone.0354305.ref029","doi-asserted-by":"crossref","first-page":"931225","DOI":"10.3389\/fpubh.2022.931225","article-title":"Artificial intelligence (AI) acceptance in primary care during the coronavirus pandemic: What is the role of patients\u2019 gender, age and health awareness? A two-phase pilot study","volume":"10","author":"H Chalutz Ben-Gal","year":"2023","journal-title":"Front Public Health"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0354305","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T17:27:46Z","timestamp":1785432466000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0354305"}},"subtitle":[],"editor":[{"given":"Mohammad","family":"Nusair","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,7,30]]},"references-count":29,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7,30]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0354305","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,30]]}}}