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Implementation of a Digital Questionnaire-Based Community Mental Health Screening System and Information Dashboard in the Bendosari Primary Healthcare Catchment Area
Abstract
Mental health is a critical component of public health; however, it remains underdetected and inadequately managed, particularly at the primary healthcare level. Following the COVID-19 pandemic, the prevalence of stress, anxiety, and depression has increased, while mental health literacy remains low and stigma persists. In the Bendosari Primary Healthcare catchment area, service coverage for severe mental disorders has not reached the national target, and structured data on community mental health conditions are lacking. This absence of systematic screening and data-driven monitoring limits the development of effective promotive and preventive programs.This community engagement project aimed to enhance mental health literacy, early detection, and monitoring capacity through the implementation of a digital screening system using the Self-Reporting Questionnaire (SRQ-20) and an information dashboard based on Google Data Studio. The method involved five integrated stages: community socialization, mental health and digital literacy training, technology implementation, mentoring and evaluation, and sustainability planning through the establishment of digital health cadres and standard operating procedures.The results demonstrated a 45.1% increase in participants’ mental health literacy scores, with 75% of participants showing improvement. Digital screening coverage reached 68% of the target population. Screening results indicated 61.8% low risk, 26.5% moderate risk, and 11.7% high risk individuals. Additionally, a functional dashboard prototype and ten digital health cadres were successfully established.In conclusion, the integration of digital screening tools and data visualization effectively improves early detection, community awareness, and data-driven decision-making in primary mental healthcare, offering a scalable model for digital health transformation.
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DOI: https://doi.org/10.37905/damhil.v5i1.38678
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