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Associations and Potential Pathways Linking Excessive Short-Video Use to Mental Health Among Vocational High School Students in China: An Integrated Analysis Based on Machine Learning and Path Analysis

Zhang Q, Huang B, Song H

Behavioral sciences (Basel, Switzerland) · 0 citas

Cómo se estudió

Diseño
Estudio transversal (clasificado por nuestro filtro de IA)
Estudiado en
Personas
Resultado principal
Marcadores de salud y funcionamiento

Quién lo pagó

Financiación
Financiación independiente

Según la declaración de financiación del texto completo.

Publicación

Publicado
2026-08-19 · Behav Sci (Basel) · vol. 16 · número 8 · pág. 1429
Editorial
Multidisciplinary Digital Publishing Institute
Citado
0 citas · más que el 76% de estudios similares · 0.0× el promedio de su campo
Referencias
51 trabajos
Acceso
Acceso abierto (revista) · CC-BY
Áreas de investigación
COVID-19 and Mental Health · Digital Mental Health Interventions · Child and Adolescent Psychosocial and Emotional Development
Palabras clave
mental health, vocational high school students, social support, coping, positive coping, negative coping, mediation pathways, predictors of mental health, screen time, adolescent mental health

3 autores

  • Qihan Zhang🇨🇳Tianjin Normal University
  • Baidong Huang🇨🇳Tianjin Normal University
  • Hongwen Song🇨🇳 · autor de correspondenciaUniversity of Science and Technology of China

Resumen

El texto original del estudio, en el idioma en que se publicó.

Excessive short-video consumption is a global concern, yet its association with mental health among vocational high school students in China remains under-researched. Among many candidate individual, psychological, and behavioral factors, it remains unclear which are the most robust predictors of mental health. This study develops a comprehensive framework to identify core predictors and examine potential pathways linking short-video use to mental health. A survey was administered to 8346 vocational high school students recruited from vocational high schools in Tianjin, China, yielding 6096 valid responses for multi-stage analysis. First, Random Forest (RF) and linear Support Vector Regression (SVR) screened 13 individual, psychological, and behavioral features to identify core predictors. Subsequently, a Path Analysis (PA) with the Bootstrap method was used to examine statistical mediating pathways involving these core features. Across RF and SVR, social support, excessive short-video use, positive coping, and negative coping emerged as the four most important predictors of mental health. PA showed generally good fit (χ2/df = 5.86, CFI = 0.998, TLI = 0.99, RMSEA = 0.03, SRMR = 0.01). Excessive short-video use was significantly and directly associated with mental health problems (effect = 0.221, 95% CI = [0.189, 0.253], 76.7% of the total association) and showed small indirect associations through three statistical pathways: (1) lower social support (effect = 0.023, 8.1%); (2) higher negative coping (effect = 0.035, 12.0%); and (3) a small serial indirect association from reduced social support to decreased positive coping (effect = 0.007, 2.6%). Findings suggest that potential intervention efforts may consider regulating short-video use while simultaneously enhancing social support and adaptive coping mechanisms as part of strategies to reduce psychological risks.

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