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Community Based Intervention01:30

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Community-based interventions in mental health represent a paradigm shift from institution-centered care to treatments embedded within the fabric of local communities. By prioritizing inclusion and leveraging existing societal structures, this approach fosters a supportive environment conducive to addressing mental health challenges while promoting individual dignity and agency.
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Levels of Health Promotion and Illness Prevention01:26

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Health promotion allows a person to control the determinants of health, resulting in an improved health status. It enhances the quality of life and reduces premature deaths. Health promotion and illness prevention programs help people make beneficial choices to reduce the risk of disease and disabilities. There are three health promotion and illness prevention levels: primary, secondary, and tertiary prevention.
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Updated: Jan 15, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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Addressing Health Disparities through Community Engagement in Artificial Intelligence-Driven Prevention Science.

Emily E Haroz1, Novalene Goklish2, Adrienne Dillard3

  • 1Center for Indigenous Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA. eharoz1@jhu.edu.

Prevention Science : the Official Journal of the Society for Prevention Research
|January 14, 2026
PubMed
Summary
This summary is machine-generated.

Community engagement is crucial for developing unbiased artificial intelligence and machine learning (AI/ML) tools in prevention science. Incorporating community input ensures AI/ML applications are equitable and effectively serve diverse populations.

Keywords:
Artificial intelligenceCommunity engagementMachine learning

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Area of Science:

  • Public Health
  • Health Equity
  • Artificial Intelligence in Healthcare

Background:

  • Artificial intelligence and machine learning (AI/ML) hold potential to either exacerbate or alleviate health inequities.
  • Community engagement is a proposed strategy to mitigate bias in AI/ML tools.
  • Integrating community perspectives is vital for equitable AI/ML development.

Purpose of the Study:

  • To outline the incorporation of community engagement throughout the AI/ML model development and implementation lifecycle.
  • To demonstrate the value of community engagement in shaping AI/ML for prevention science.
  • To provide practical examples of community-engaged AI/ML in health equity initiatives.

Main Methods:

  • Framework adaptation from prevention research phases to guide community engagement in AI/ML.
  • Case study analysis of real-world AI/ML applications with community involvement.
  • Qualitative synthesis of community-driven AI/ML development processes.

Main Results:

  • Community engagement can be integrated at all stages of AI/ML development and implementation.
  • Examples include suicide prevention with Indigenous communities, chronic disease prevention for Hispanic/Latino populations, and resource allocation for Native Hawaiians.
  • Community-driven AI/ML efforts show promise in addressing social determinants of health.

Conclusions:

  • Community engagement is critical for ensuring AI/ML tools are relevant, rigorous, and reach underserved populations.
  • Prioritizing community voices in AI/ML development is essential for advancing health equity.
  • Collaborative approaches are necessary to leverage AI/ML responsibly in prevention science.