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Surveys are essential for marking property boundaries near water bodies. Different types of surveys are defined, each with its own function. Land surveys mark the property boundaries, while route surveys determine the position of properties on nearby highways. Topographic surveys create maps by capturing the three-dimensional features of the land. Hydrographic surveys focus on the shapes of underwater areas and the movement of streams through the properties. Mine surveys determine the relative...
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Surveying near highways, rough terrain, or power lines involves significant risks. Working along highways is particularly dangerous and requires the use of warning signs and flagmen. It is safest to avoid working directly on roads and use offsets whenever possible. When highway work is unavoidable, it must follow all safety guidelines. Surveyors should wear bright clothing, such as orange reflective vests, to ensure visibility to motorists, coworkers, and hunters. In construction zones, wearing...
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Predicting the Intention to Use Generative Artificial Intelligence for Health Information: Comparative Survey Study.

Jörg Matthes1, Anne Reinhardt2, Selma Hodzic1

  • 1Department of Communication, University of Vienna, Waehringer Street 29, Vienna, 1090, Austria, 43 14277493.

Journal of Medical Internet Research
|January 28, 2026
PubMed
Summary
This summary is machine-generated.

Generative AI for health information is currently used by early adopters who find it useful and enjoyable. Future efforts should focus on usefulness and accessibility to ensure equitable digital health equity.

Keywords:
AI adoptionUTAUT2Unified Theory of Acceptance and Use of Technology 2artificial intelligencegenerative AIhealth information–seeking

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Area of Science:

  • Digital Health
  • Artificial Intelligence
  • Health Communication

Background:

  • Generative artificial intelligence (AI) tools are transforming online information access.
  • In healthcare, generative AI presents a disruptive potential due to accessibility and tailored content.
  • Questions remain regarding generative AI's health information usage and its impact on digital health equity.

Purpose of the Study:

  • Assess the relevance of generative AI as a health information source.
  • Identify factors predicting the intention to use generative AI for health.
  • Compare adoption factors across four European countries.

Main Methods:

  • Online survey of 1990 participants aged 16-74 across Austria, Denmark, France, and Serbia.
  • Applied the Unified Theory of Acceptance and Use of Technology 2, extended with health literacy and health status.
  • Structural equation modeling with metric measurement invariance tested cross-national associations.

Main Results:

  • Generative AI health information usage was limited (39.5% rarely used).
  • Medical experts and search engines remain primary health information sources.
  • Performance expectancy, habit, and hedonic motivation significantly predicted intention to use generative AI for health across all countries.

Conclusions:

  • Current generative AI adoption for health is driven by early adopters finding it useful, convenient, and enjoyable.
  • Consistent cross-national adoption patterns suggest a shared European trend.
  • Promoting equitable adoption requires focusing on usefulness, convenience, enjoyment, digital access, and safeguards for vulnerable users.