ReviewFrontiers in public health2022
Measuring the impact of the COVID-19 epidemic on university resumption and suggestions for countermeasures.
Review in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Data-driven strategies for model-informed decision-making during the COVID-19 pandemic: a systematic review.BMJ open · 2026Pooled it
- Pre-exam anxiety during COVID-19: A cross-sectional study of medical and paramedical students in the Arab community.Medicine · 2025Article
- Article
- Impact of epidemic prevention policies on public vaccination willingness: empirical research in China.Frontiers in public health · 2024Article
- Navigating the Adoption Maze: Evolutionary Dynamics of Stakeholder Behavior in AI-Driven Elderly Care Solutions.Inquiry : a journal of medical care organization, provision and financingArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: During the COVID-19 pandemic, universities around the world had to find a balance between the need to resume classes and prevent the spread of the virus by ensuring the health of students. The purpose of our study was to effectively assess the overall risk of universities reopening during the COVID-19 epidemic. Design and methods: Using the pressure-state-response model, we designed a risk evaluation method from a disaster management perspective. First, we performed a literature review to find the main factors affecting the virus spread. Second, we used the pressure-state-response to represent how the considered hazards acts and interacts before grouping them as disaster and vulnerability factors. Third, we assigned to all factors a risk function ranging from 1 to 4. Fourth, we modeled the risk indexes of disaster and of system vulnerability through simple and appropriate weights and combined them in an overall risk for the university resumption. Finally, we showed how the method works by evaluating the reopening of the Hebei Province University in 2022 and highlighted the resulting advice for reducing related risks. Results: Our model included 20 risk factors, six representing exogenous hazards (disaster factors) that university can only monitor and 14 related to system vulnerability that can also control. Disaster factors included epidemic risk level of students' residence and the school's location, means of transportation back to school, size of the university population, the number of migrants on and off campus and express carrier infection. Vulnerability factors included student behaviors, routine campus activities and all the other actions the university can take to control the virus spread. The university of Baoding city (Hebei Province) showed a disaster risk of 1.880 and a vulnerability of 1.666 which combined provided a low risk of school resumption. Conclusion: Our study judged the risks involved in resuming school and put forward specific countermeasures for reducing the risk levels. This not only protects public health security but also has some practical implications for improving the evaluation and rational decision-making abilities of all parties.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.