Predicting HIV self-testing intentions among Chinese college students: a dual-model analysis integrating health belief constructs and machine learning prioritization.

ABSTRACT:

Introduction

As 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.

Methods

This 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 ana

SUBMITTER: Jiang Y 

PROVIDER: S-EPMC12351388 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature