Predicting HIV self-testing intentions among Chinese college students: a dual-model analysis integrating health belief constructs and machine learning prioritization
IntroductionAs college students emerge as a key HIV-vulnerable population in China, HIV self-testing (HIVST) presents a critical strategy for enhancing detection rates and enabling timely intervention. While observational studies have identified multifactorial influences on HIVST willingness, few in...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1596876/full |
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| author | Yan Jiang Jing Li Jing Li Jingfen Lu Liping He Lin Hu Jiazhen He Xianli Huang Yuchao Li |
| author_facet | Yan Jiang Jing Li Jing Li Jingfen Lu Liping He Lin Hu Jiazhen He Xianli Huang Yuchao Li |
| author_sort | Yan Jiang |
| collection | DOAJ |
| description | IntroductionAs college students emerge as a key HIV-vulnerable population in China, HIV self-testing (HIVST) presents a critical strategy for enhancing detection rates and enabling timely intervention. While observational studies have identified multifactorial influences on HIVST willingness, few investigations integrate behavioral theory with machine learning approaches among college students. This study aims to fill this gap by exploring the determinants of HIVST willingness among college students using the Health Belief Model (HBM) and random forest analytics.MethodsThis cross-sectional study employed stratified cluster sampling to recruit 1,015 undergraduates from Xiangnan College (July-August 2022), The Health Belief Model (HBM) was synthesized with random forest analytics to elucidate determinants of HIVST willingness. Data were collected through questionnaires, and logistic regression and random forest modeling were used for analysis.ResultsAmong participants, 69.3% (n = 703) expressed willingness to adopt HIVST within the next 6 months. 15.0% reported sexual activity (n = 152), with 12.0% (n = 122) of sexually active participants demonstrating concurrent engagement in unprotected intercourse and HIV testing willingness. HBM-based logistic regression revealed that self-efficacy (OR = 1.64, 95% CI: 1.21-2.21) and cues to action (OR = 1.34, 1.04-1.75) were significant facilitators, contrasting with the inhibitory effects of perceived barriers (OR = 0.69, 0.55-0.86). Random forest modeling prioritized these psychological constructs (mean decrease Gini >2.5), identifying male students and arts majors as critical subpopulations requiring targeted intervention.DiscussionOur dual-method analysis establishes that campus HIV control necessitates: 1) Gender-specific prevention programs addressing male students’ elevated risk exposure; 2) HBM-informed education strengthening self-efficacy and environmental cues; 3) Structural interventions reducing testing barriers through discreet service delivery. This theoretical-empirical integration advances predictive understanding of HIVST behaviors, providing actionable insights for developing precision public health strategies in academic settings. |
| format | Article |
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| institution | Kabale University |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Public Health |
| spelling | doaj-art-d28d3f93cb294002bfffe4ba941cef162025-08-20T03:58:36ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-07-011310.3389/fpubh.2025.15968761596876Predicting HIV self-testing intentions among Chinese college students: a dual-model analysis integrating health belief constructs and machine learning prioritizationYan Jiang0Jing Li1Jing Li2Jingfen Lu3Liping He4Lin Hu5Jiazhen He6Xianli Huang7Yuchao Li8School of Public Health, Xiangnan University, Chenzhou, ChinaSchool of Public Health, Xiangnan University, Chenzhou, ChinaDepartment of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, ChinaThe First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, ChinaSchool of Public Health, Xiangnan University, Chenzhou, ChinaSchool of Public Health, Xiangnan University, Chenzhou, ChinaSchool of Public Health, Xiangnan University, Chenzhou, ChinaSchool of Public Health, Xiangnan University, Chenzhou, ChinaCollege of Education Hunan University of Humanities, Science and Technology, Loudi, ChinaIntroductionAs college students emerge as a key HIV-vulnerable population in China, HIV self-testing (HIVST) presents a critical strategy for enhancing detection rates and enabling timely intervention. While observational studies have identified multifactorial influences on HIVST willingness, few investigations integrate behavioral theory with machine learning approaches among college students. This study aims to fill this gap by exploring the determinants of HIVST willingness among college students using the Health Belief Model (HBM) and random forest analytics.MethodsThis cross-sectional study employed stratified cluster sampling to recruit 1,015 undergraduates from Xiangnan College (July-August 2022), The Health Belief Model (HBM) was synthesized with random forest analytics to elucidate determinants of HIVST willingness. Data were collected through questionnaires, and logistic regression and random forest modeling were used for analysis.ResultsAmong participants, 69.3% (n = 703) expressed willingness to adopt HIVST within the next 6 months. 15.0% reported sexual activity (n = 152), with 12.0% (n = 122) of sexually active participants demonstrating concurrent engagement in unprotected intercourse and HIV testing willingness. HBM-based logistic regression revealed that self-efficacy (OR = 1.64, 95% CI: 1.21-2.21) and cues to action (OR = 1.34, 1.04-1.75) were significant facilitators, contrasting with the inhibitory effects of perceived barriers (OR = 0.69, 0.55-0.86). Random forest modeling prioritized these psychological constructs (mean decrease Gini >2.5), identifying male students and arts majors as critical subpopulations requiring targeted intervention.DiscussionOur dual-method analysis establishes that campus HIV control necessitates: 1) Gender-specific prevention programs addressing male students’ elevated risk exposure; 2) HBM-informed education strengthening self-efficacy and environmental cues; 3) Structural interventions reducing testing barriers through discreet service delivery. This theoretical-empirical integration advances predictive understanding of HIVST behaviors, providing actionable insights for developing precision public health strategies in academic settings.https://www.frontiersin.org/articles/10.3389/fpubh.2025.1596876/fullhealth belief modelcollege studentHIV self-testinghigh-risk behaviorsrandom forest modeling |
| spellingShingle | Yan Jiang Jing Li Jing Li Jingfen Lu Liping He Lin Hu Jiazhen He Xianli Huang Yuchao Li Predicting HIV self-testing intentions among Chinese college students: a dual-model analysis integrating health belief constructs and machine learning prioritization Frontiers in Public Health health belief model college student HIV self-testing high-risk behaviors random forest modeling |
| title | Predicting HIV self-testing intentions among Chinese college students: a dual-model analysis integrating health belief constructs and machine learning prioritization |
| title_full | Predicting HIV self-testing intentions among Chinese college students: a dual-model analysis integrating health belief constructs and machine learning prioritization |
| title_fullStr | Predicting HIV self-testing intentions among Chinese college students: a dual-model analysis integrating health belief constructs and machine learning prioritization |
| title_full_unstemmed | Predicting HIV self-testing intentions among Chinese college students: a dual-model analysis integrating health belief constructs and machine learning prioritization |
| title_short | Predicting HIV self-testing intentions among Chinese college students: a dual-model analysis integrating health belief constructs and machine learning prioritization |
| title_sort | predicting hiv self testing intentions among chinese college students a dual model analysis integrating health belief constructs and machine learning prioritization |
| topic | health belief model college student HIV self-testing high-risk behaviors random forest modeling |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1596876/full |
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