How much of this occupation’s work can be materially affected by current AI systems.
How likely exposure is to translate into reduced human demand.
Includes provisional estimates for AI adoption pressure and labour-market resilience. How this is measured
Confidence reflects task coverage, mapping and capability-evidence quality, and how much of the score rests on provisional inputs.
What is driving the score?
Occupation scores are built from the task mix—not a single prediction about a job title.
Where AI can do more
Routine, digitized, and highly repeatable tasks face the greatest pressure.
- Administer immunizations or other basic preventive treatments.74
- Identify or contact members of high-risk or otherwise targeted groups, such as members of minority populations, low-income populations, or pregnant women.74
- Identify the particular health care needs of individuals in a community or target area.74
- Interpret, translate, or provide cultural mediation related to health services or information for community members.74
Where people still matter
These tasks score lowest on automation feasibility—physical presence, judgement, accountability and real-world variability all resist end-to-end automation.
- Monitor nutrition of children, elderly, or other high-risk groups.01
- Conduct home visits for pregnant women, newborn infants, or other high-risk individuals to monitor their progress or assess their needs.02
- Perform basic diagnostic procedures, such as blood pressure screening, breast cancer screening, or communicable disease screening.03
- Administer immunizations or other basic preventive treatments.04
- Identify or contact members of high-risk or otherwise targeted groups, such as members of minority populations, low-income populations, or pregnant women.05
Related occupations
Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
Health Education Specialists
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Related work
Compare these careers →Adoption and labour-market outlook
Structural factors are kept separate from raw capability so you can see what actually resists automation. Adoption pressure and labour-market resilience are still provisional models—25% of this occupation’s replacement-risk weight rests on them.
O*NET 30.3 occupational data interpreted through the JobsVsAI capability, automation and structural-constraint models.
