Academic Performance and the Link with Depressive Symptoms among Rural Han and Minority Chinese Adolescents
Tianli Feng,
Xiyuan Jia,
Lucy Pappas,
Xiaojun Zheng,
Teresa Shao,
Letao Sun,
Charlie Weisberg,
Madeline Lu Li,
Scott Rozelle and
Yue Ma
Additional contact information
Tianli Feng: School of Management and Economics, University of Electronic Science and Technology of China, Chengdu 610054, China
Xiyuan Jia: School of Public Administration, Northwest University, Xi’an 710069, China
Lucy Pappas: Stanford Center on China’s Economy and Institutions, Stanford University, Stanford, CA 94305, USA
Xiaojun Zheng: Suide Middle School, Yulin 718000, China
Teresa Shao: Stanford Center on China’s Economy and Institutions, Stanford University, Stanford, CA 94305, USA
Letao Sun: Stanford Center on China’s Economy and Institutions, Stanford University, Stanford, CA 94305, USA
Charlie Weisberg: Stanford Center on China’s Economy and Institutions, Stanford University, Stanford, CA 94305, USA
Madeline Lu Li: College of Letters & Science, University of California Santa Barbara, Santa Barbara, CA 93106, USA
Yue Ma: Stanford Center on China’s Economy and Institutions, Stanford University, Stanford, CA 94305, USA
IJERPH, 2022, vol. 19, issue 10, 1-20
Abstract:
The objectives of this paper were to examine the risk of depression and depressive symptoms among Han and minority children and adolescents in rural China, the links between academic performance and depressive symptoms, and the prevalence of these links among specific subgroups. A total of 8392 4th, 5th, and 6th grade students at 105 sample rural schools in eight low-income counties and districts in a prefectural-level city in Southwestern China were randomly selected using a three-step sampling strategy. A total of 51% of the sample were female (SD = 0.50), and the age range was 7 to 19 years (mean = 11.35 years; SD = 1.05). Using the Patient Health Questionnaire 8-item depression scale, the prevalence of depressive symptoms in the sample was assessed, while data on students’ academic performance (standardized math test) and demographic characteristics were also collected. Our results show that the rates of major depression were 19% for Han students, 18% for Tibetan students, and 22% for Yi students; the rates of severe depression were 2% for Han and Tibetan students, and 3% for Yi students. Yi students were at significantly higher risks for major and severe depression than Han students. We conducted multivariate regression and heterogeneous analyses. Academic performance was negatively and significantly correlated to depressive symptoms. Across the whole sample, students with lower math scores, minority students, boys, younger students, and students with migrant parents were most vulnerable to depressive symptoms. The heterogeneous analysis suggests that among poor-performing students, subgroups at higher risk for depression include boys, non-boarding students, and students whose mothers had graduated from high school or above. These findings indicate a need to improve mental health outcomes of rural Han and minority primary school students, targeting academic performance for possible intervention.
Keywords: depressive symptoms; academic performance; China; rural Han students; rural minority students; children and adolescents (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://www.mdpi.com/1660-4601/19/10/6026/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/10/6026/ (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:19:y:2022:i:10:p:6026-:d:816290
Access Statistics for this article
IJERPH is currently edited by Ms. Jenna Liu
More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().