Precision Mental Healthcare: Identifying Service Preferences Through Discrete-Choice Experiments in Chinese Megacities

Designing mental health services that align with individual preferences is a cornerstone of patient-centered care, enhancing both service utilization and treatment outcomes. This study applied the precision framework of mental healthcare to explore preferences for first-contact mental health services among community residents and family members with mild psychiatric symptoms in Chinese megacities. Using a discrete choice experiment, an online survey was conducted with 4,057 participants from Beijing, Shanghai, Guangzhou, and Shenzhen. Mixed logit analysis identified service providers as a key determinant of mental health service utilization across cities. While both potential patients and family members favored lower-cost public services, notable variations emerged in preferences for psychological counseling/therapy across cities and between patient and family contexts. Latent class analysis further revealed four distinct preference groups among potential patients. Psychological distress levels and hukou type were significantly associated with preferences. These findings highlight the importance of tailored interventions that accommodate patient and family needs, leveraging the unique features of China's mental healthcare system to improve access, quality, and equity in mental health services.

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Publication Details

Journal
Administration and Policy in Mental Health and Mental Health Services Research
Published
2025-04-22
DOI
https://doi.org/10.1007/s10488-025-01444-z
Citations
1
Primary Topic
Patient Satisfaction in Healthcare
Type
article
Field-Weighted Citation Impact
2.82

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article

Precision Mental Healthcare: Identifying Service Preferences Through Discrete-Choice Experiments in Chinese Megacities

Bo Li, Juan Chen, Jie Yan, Liying Ren et al.
1 citations
Administration and Policy in Mental Health and Mental Health Services Research
Patient Satisfaction in Healthcare
2.82
article

Precision Mental Healthcare: Identifying Service Preferences Through Discrete-Choice Experiments in Chinese Megacities

Bo Li, Juan Chen, Jie Yan, Liying Ren, Luqi Yuan
article en
1 citations

Abstract

Designing mental health services that align with individual preferences is a cornerstone of patient-centered care, enhancing both service utilization and treatment outcomes. This study applied the precision framework of mental healthcare to explore preferences for first-contact mental health services among community residents and family members with mild psychiatric symptoms in Chinese megacities. Using a discrete choice experiment, an online survey was conducted with 4,057 participants from Beijing, Shanghai, Guangzhou, and Shenzhen. Mixed logit analysis identified service providers as a key determinant of mental health service utilization across cities. While both potential patients and family members favored lower-cost public services, notable variations emerged in preferences for psychological counseling/therapy across cities and between patient and family contexts. Latent class analysis further revealed four distinct preference groups among potential patients. Psychological distress levels and hukou type were significantly associated with preferences. These findings highlight the importance of tailored interventions that accommodate patient and family needs, leveraging the unique features of China's mental healthcare system to improve access, quality, and equity in mental health services.

Administration and Policy in Mental Health and Mental Health Services ResearchVol. 52(4)
Hong Kong Polytechnic University (HK), Peking University (CN), Beijing Municipal Government (CN), Institute of Atmospheric Physics (CN)
Hong Kong Polytechnic University
Openalex Percentile: Top 12%
Patient Satisfaction in Healthcare
2.82
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Precision Mental Healthcare: Identifying Service Preferences Through Discrete-Choice Experiments in Chinese Megacities — Bo Li, Juan Chen, et al. · Administration and Policy in Mental Health and Mental Health Services Research (2025) | TGRS Research Map | TGRS