Mental Health Promotion Model: Mixed-Method Study of Adverse Childhood Experience and Neurovegetative in Early Adulthood
Abstract
Introduction: In this study, we aim to analyze the relationship between mental health, adverse childhood experiences, and neurovegetative factors in early adulthood. The findings created a mental health promotion model that considers aspects of digital media use and direct psychological approaches.
Methods: The study used a mixed-method approach with an explanatory sequential design. Quantitative participant data were collected using non-probability sampling from 460 individuals (male = 85; female = 375; mean age: 18–20 years), while qualitative data were collected from 20 informants (4 = HIMPSI, 9 = Academician, 7 = General). The instruments used were the MHI-38, ACE Scale, NCQ, interview guide, and FGD. Quantitative data analysis was conducted using path analysis with SmartPLS 4.0 software. In contrast, qualitative data analysis involves reduction, data display, and drawing conclusions. This data influenced the design of the mental health promotion model.
Results: The results of this study found that adverse childhood experiences (ACE) and neurovegetative factors influence mental health in early adulthood (P = 0.001), with ACE (P = 0.009) and neurovegetative factors (P = 0.000), with ACE also specifically acting as a predictor for neurovegetative factors (P = 0.00). These results prompted the development of a mental health model within the context of the three variables, considering key challenges, effective strategies, reaching vulnerable groups, and model recommendations by experts. The final model consists of a digital scheme and a direct approach model, incorporating interventions such as infographics, videos, psychoeducation, community development, and VR-CBT.
Conclusion: This study describes the urgency of mental health stability by considering ACE and neurovegetative as predictors. The data contribute to the development of a mental health promotion model in early adulthood. Implementation of the model in various settings is recommended.
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