Related Experiment Videos
Multifactorial predictive model for nurses' intention to correct online health misinformation: A machine-learning and
Pei Duan1, Zhao Liu2, Song Chen1
1School of Nursing, Faculty of Medicine, Yangzhou University, Yangzhou, 225009, PR China.
Public Health
|July 1, 2026
Summary
Nurses intend to correct online health misinformation, driven by recognizing harm, professional competence, and clinical context. Enhancing eHealth literacy and risk communication skills can bolster frontline responses to misinformation.
Area of Science:
- Health Informatics
- Nursing Research
- Artificial Intelligence in Healthcare
Background:
- Online health misinformation poses a significant threat to public health.
- Nurses are crucial frontline healthcare professionals who can combat misinformation.
- Understanding factors influencing nurses' intention to correct misinformation is vital for intervention development.
Purpose of the Study:
- To develop and interpret a predictive model identifying key factors influencing nurses' intention to rectify online health misinformation.
- To provide an evidence base for targeted interventions to strengthen frontline responses to health misinformation.
- To integrate machine learning with explainable AI for a comprehensive analysis.
Main Methods:
- A nationwide cross-sectional online survey of 1120 registered nurses in China.
- Machine learning models (ExtraTrees) were trained and validated using stratified data splitting and resampling techniques.
- SHapley Additive exPlanations (SHAP) and Structural Equation Modelling (SEM) were used for model interpretation and validation.
Main Results:
- 80.2% of nurses intended to rectify online health misinformation.
- The ExtraTrees model achieved high performance (AUC=0.919, accuracy=0.827).
- Key predictors included perception of harm, general ward work, education, trustworthiness, experience, and eHealth literacy, clustered into risk perception, professional competence, and clinical context.
Conclusions:
- Nurses' intention to correct misinformation is influenced by recognizing harm, critical appraisal skills, and digital competencies within their practice settings.
- Enhancing eHealth literacy and risk communication skills can improve nurses' capacity to mitigate misinformation's public health impact.
- A combined predictive modeling and theory-driven approach offers a robust framework for understanding and addressing health misinformation.
Related Concept Videos
01:25Models 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...
The health belief model (HBM) attempts to predict health-related behavior in specific belief patterns. According to the HBM, a person's...
01:30Current Trends in Nursing II
Trends in nursing are multifactorial and associated with changes in society, within the nursing profession, and in other professions. Notably, telehealth and remote nursing contribute to successful healthcare delivery for numerous patients and help reduce stress for nurses due to nursing shortages. Nurses can reach patients, monitor their conditions, and interact with them using computers, audio, visual accessories, and telephones—for example, remote patient monitoring systems. Likewise,...