Why did individuals seeking Covid-19 advice return to internet hospitals for follow-up in the early pandemic
Insights from machine learning
Bibliographic Data
| ID | 15364133 |
|---|---|
| Authors | Xiaopu Zhang (0000-0002-9647-1477, Changzhou Third People's Hospital, corresponding author), Qiang Wang (0000-0003-3091-5503, Changzhou No.2 People's Hospital), Ling Yang (0000-0001-5750-6588, Soochow University), Tong Su (0000-0002-3853-8506, Soochow University) |
| Year | 2025 |
| Volume | 25 |
| Issue | 1 |
| Pages | 3867-3867 |
| Publication date | 2025-11-10 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | BMC Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1471-2458 • E-ISSN: 1471-2458 |
| Publisher | BioMed Central (PUBLISHER • GB) |
| DOI | 10.1186/s12889-025-25137-2 |
| PMID | 41214637 |
| OpenAlex | W4416092856 |
| Language | EN |
| Citations received | 1 |
| References cited | 40 |
Machine learning may help identify factors influencing the choice of internet hospitals for follow-up consultations. Integrating the Health Belief Model enables a more nuanced understanding of individuals' online consultation behaviors. Optimizing triage services could enhance the quality and accessibility of internet hospitals, offering valuable insights for future public health events
Advice (programming · Biostatistics · Public health · Quality (philosophy · Ranking (information retrieval · The Internet · Triage · Artificial Intelligence in Healthcare and Education · COVID-19 and Mental Health · COVID-19 Digital Contact Tracing
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| Citations per year | 1 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |