Hofmann Bjørn
Department of Health Sciences, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Gjøvik, Norway.
Centre for Medical Ethics, Faculty of Medicine, University of Oslo, Oslo, Norway.
J Eval Clin Pract. 2020 Oct;26(5):1344-1346. doi: 10.1111/jep.13443. Epub 2020 Jul 22.
The COVID-19 has posed a wide range of urgent questions: about the disease, testing, immunity, treatments, and outcomes. Extreme situations, such as pandemics, call for exceptional measures. However, this threatens the production and application of evidence.
This article applies standard categories in epistemology to analyse the pandemic in terms of four kinds of uncertainty: Risk, Fundamental uncertainty, Ignorance, and Ambiguity.
Mapping the uncertainties of the pandemic onto the four types of uncertainty directs evidence production towards specific tasks in order to address the challenges of the pandemic: Eliminating ambiguity, being alert to the unknown, and gathering data to estimate risks are crucial to preserve evidence and save lives.
In order to avoid fake facts and to provide sustainable solutions, we need to pay attention to the various kinds of uncertainty. Producing high-quality evidence is the solution, not the problem.
新冠疫情引发了一系列紧迫问题：关于疾病、检测、免疫、治疗及结果。诸如大流行这样的极端情况需要采取特殊措施。然而，这对证据的产生和应用构成了威胁。
本文运用认识论中的标准类别，从风险、根本不确定性、无知和模糊性这四种不确定性角度分析这场大流行。
将大流行的不确定性映射到这四种不确定性类型上，可引导证据的产生指向特定任务，以应对大流行带来的挑战：消除模糊性、警惕未知情况以及收集数据以估计风险，对于保存证据和拯救生命至关重要。
为避免虚假事实并提供可持续的解决方案，我们需要关注各种不确定性。产生高质量证据是解决之道，而非问题所在。
