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<p>Understanding and predicting sexual behavior patterns through the lens of Social Cognitive Theory (SCT) is essential for identifying behavioral bridges that facilitate HIV transmission. MSM who engage in sexual activity with women (MSM-W) represent a critical yet underexplored subgroup in China. However, existing studies lack theory-driven predictive frameworks that explicitly integrate psychological mechanisms with robust analytics. This study aimed to develop and validate an SCT-guided machine-learning model to predict MSM-W behavior and to evaluate its discriminatory and calibration performance across population subgroups. We developed a theory-driven machine-learning framework integrating SCT with a random forest–guided logistic regression model. Data were collected from 2,403 MSM across six urban regions in China. Model development included: (1) SCT-guided candidate variable construction, (2) random-forest feature screening using mean decrease accuracy (MDA), (3) nested cross-validation and bootstrap optimism correction, (4) calibration assessment, and (5) threshold selection using the Youden index. Model performance was evaluated using ROC-AUC, PR-AUC, Brier score, calibration slope/intercept, subgroup analyses (age, education, marital status, migrant status), and comparison with a baseline demographic-only model. Among all participants, 406 (16.9%) reported sexual behavior with women in the past six months. The final SCT-guided model incorporating nine predictors (GAD-7, PHQ-9, self-esteem, age, education level, marital status, sexual orientation, recent group sex with men, and pre-sex drug use) demonstrated good discrimination (AUC = 0.80), moderate PR-AUC (0.53), and good overall accuracy (Brier score = 0.107). Calibration analysis showed a slope of 0.91 (95% CI: 0.76–1.09) and intercept of − 0.20 (95% CI: −0.53 to 0.15), indicating acceptable agreement between predicted and observed risks. The optimal probability threshold selected by the Youden index was 0.148 (sensitivity 0.72; specificity 0.77; NPV 0.93). Subgroup analyses demonstrated stable performance across age, education, marital, and migrant strata. Compared with the baseline demographic-only model (AUC = 0.76; Brier = 0.113), the full SCT-integrated model showed consistent improvement. Results were translated into an individualized nomogram for practical risk assessment. This study provides a theory-informed and statistically robust framework for predicting MSM-W behavior. By integrating SCT with machine learning, the model captures the joint influence of psychological distress, self-evaluation, and behavioral context on bisexual behavior patterns. The tool offers a non-stigmatizing, early-screening approach for identifying behavioral risk bridges in MSM populations and may inform targeted HIV prevention strategies in China.</p>

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Integrating social cognitive theory with machine learning to predict MSM-women sexual behavior: a multicenter random forest model development study in China

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月22日

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  • Shangbin Liu,
  • Ying Gao,
  • Huifang Xu,
  • Quyige Gao,
  • Gang Xu,
  • Jianyu Chen,
  • Jiechen Zhang,
  • Ying Wang,
  • Yong Cai

摘要

Understanding and predicting sexual behavior patterns through the lens of Social Cognitive Theory (SCT) is essential for identifying behavioral bridges that facilitate HIV transmission. MSM who engage in sexual activity with women (MSM-W) represent a critical yet underexplored subgroup in China. However, existing studies lack theory-driven predictive frameworks that explicitly integrate psychological mechanisms with robust analytics. This study aimed to develop and validate an SCT-guided machine-learning model to predict MSM-W behavior and to evaluate its discriminatory and calibration performance across population subgroups. We developed a theory-driven machine-learning framework integrating SCT with a random forest–guided logistic regression model. Data were collected from 2,403 MSM across six urban regions in China. Model development included: (1) SCT-guided candidate variable construction, (2) random-forest feature screening using mean decrease accuracy (MDA), (3) nested cross-validation and bootstrap optimism correction, (4) calibration assessment, and (5) threshold selection using the Youden index. Model performance was evaluated using ROC-AUC, PR-AUC, Brier score, calibration slope/intercept, subgroup analyses (age, education, marital status, migrant status), and comparison with a baseline demographic-only model. Among all participants, 406 (16.9%) reported sexual behavior with women in the past six months. The final SCT-guided model incorporating nine predictors (GAD-7, PHQ-9, self-esteem, age, education level, marital status, sexual orientation, recent group sex with men, and pre-sex drug use) demonstrated good discrimination (AUC = 0.80), moderate PR-AUC (0.53), and good overall accuracy (Brier score = 0.107). Calibration analysis showed a slope of 0.91 (95% CI: 0.76–1.09) and intercept of − 0.20 (95% CI: −0.53 to 0.15), indicating acceptable agreement between predicted and observed risks. The optimal probability threshold selected by the Youden index was 0.148 (sensitivity 0.72; specificity 0.77; NPV 0.93). Subgroup analyses demonstrated stable performance across age, education, marital, and migrant strata. Compared with the baseline demographic-only model (AUC = 0.76; Brier = 0.113), the full SCT-integrated model showed consistent improvement. Results were translated into an individualized nomogram for practical risk assessment. This study provides a theory-informed and statistically robust framework for predicting MSM-W behavior. By integrating SCT with machine learning, the model captures the joint influence of psychological distress, self-evaluation, and behavioral context on bisexual behavior patterns. The tool offers a non-stigmatizing, early-screening approach for identifying behavioral risk bridges in MSM populations and may inform targeted HIV prevention strategies in China.