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Models of Health Promotion and Illness Prevention I01:25

Models of Health Promotion and Illness Prevention I

A model is a theoretical way to understand a concept or an idea. Models can overcome barriers to health regardless of diverse economic and cultural backgrounds. In addition, models make the task easier by providing different ways to approach complex issues. There are two major health promotion models: the health belief model and the health promotion model.
The health belief model (HBM) attempts to predict health-related behavior in specific belief patterns. According to the HBM, a person's...
Dimensions of Health and Illness01:21

Dimensions of Health and Illness

The factors influencing the health-illness continuum can be internal or external and may or may not be under conscious control. They are related to the following eight human dimensions, and each dimension is interrelated to one other.
Concepts of Health and Illness01:29

Concepts of Health and Illness

Health is a condition of the body, mind, and spirit where an individual remains free from illness. Similarly, wellness is an active state, including living a lifestyle that promotes physical, mental, and emotional health. Physical health is critical for the overall well-being and can be affected by lifestyle, activity level, diet, and behavior. The highest attainable standard of health is a fundamental and universal human right. Consider Lisa, a fifteen-year-old born with congenital...
Models of Health Promotion and Illness Prevention II01:18

Models of Health Promotion and Illness Prevention II

The person's health status fluctuates continually, varying from being in good health to becoming ill and returning to being healthy. To understand the concept of illness prevention, there are two models. First, the health-illness continuum model is a graphic representation of an individual's wellness. It states that a person is considered healthy in the absence of physical disease and the presence of good emotional health.
The agent-host-environment model states that disease results from...
Social Cognitive Perspective on Personality01:30

Social Cognitive Perspective on Personality

Social cognitive perspectives on personality emphasize the importance of conscious awareness, beliefs, expectations, and goals in shaping behavior. These perspectives incorporate behaviorist principles, such as learning through reinforcement and conditioning, but extend beyond them by highlighting human reasoning and planning. Unlike traditional behaviorist views, social cognitive theory focuses on how individuals reflect on their past experiences and plan for future outcomes by considering...
Factors Affecting Illness01:18

Factors Affecting Illness

When a person's physical, emotional, intellectual, social development or spiritual functioning is compromised, this deviation from a healthy normal state is called illness. Illness creates stress that in turn harms individuals. Irritation, anger, denial, hopelessness, and fear are behavioral and emotional changes an individual experiences in the phases of illness. A variety of factors influence a person's health and well-being.
For instance, risk factors are connected to illness, disability,...

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Perspective: Machine Learning for Health Should Consider Social Drivers of Health.

Neha Srivathsa1, Sherri Rose2

  • 1Department of Computer Science, Stanford University School of Engineering.

Proceedings of Machine Learning Research
|June 17, 2026
PubMed
Summary
This summary is machine-generated.

Clinical machine learning (ML) algorithms can worsen health inequities, disproportionately harming minoritized communities. Addressing social drivers of health (SDOH) in ML development is crucial for promoting equity, though root causes require broader interventions.

Keywords:
algorithmic harmscommunity-engaged researchhealth inequitiesmachine learning for healthsocial drivers of healthstructural analysisstructural drivers of health

Related Experiment Videos

Area of Science:

  • Health Informatics
  • Artificial Intelligence in Medicine
  • Health Equity Research

Background:

  • Clinical machine learning (ML) algorithms can perpetuate societal injustices.
  • Disparities exist in the distribution of harms and benefits of health ML, with minoritized groups often bearing the brunt.
  • Algorithmic harms in healthcare are frequently underemphasized and inadequately addressed.

Purpose of the Study:

  • To analyze the relationship between algorithmic harms and social drivers of health (SDOH).
  • To highlight the tension between ML development in health and the potential to exacerbate health inequities.
  • To propose strategies for mitigating algorithmic harms and promoting health equity through ML.

Main Methods:

  • Framework analysis correlating algorithmic harm typologies with social drivers of health (SDOH) concepts.
  • Examination of how algorithmic harms impact human health via SDOH factors, particularly structural determinants.
  • Literature review on existing approaches to algorithmic bias and health equity.

Main Results:

  • Algorithmic harms impact health primarily through social drivers of health (SDOH), especially structural factors.
  • The development of ML for health inherently risks worsening existing health inequities.
  • A significant tension exists between the goals of ML in health and the imperative to reduce health disparities.

Conclusions:

  • Integrating SDOH considerations throughout the ML development pipeline is essential for identifying and addressing algorithmic harms.
  • Developing expertise in structural analysis and community-engaged methodologies is necessary for effectively considering SDOH.
  • While SDOH can guide the creation of more equitable algorithms, interventions targeting the root causes of health inequities are paramount, as equitable algorithms may not always be feasible.