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利用智能手机在自然环境中研究疫苗接种决策。

Using smartphones to study vaccination decisions in the wild.

作者信息

Girardini Nicolò Alessandro, Stopczynski Arkadiusz, Baranov Olga, Betsch Cornelia, Brockmann Dirk, Lehmann Sune, Böhm Robert

机构信息

Department of Information Engineering and Computer Science (DISI), University of Trento, Italy.

Mobile and Social Computing Lab (MobS), Fondazione Bruno Kessler (FBK), Trento, Italy.

出版信息

PLOS Digit Health. 2024 Aug 8;3(8):e0000550. doi: 10.1371/journal.pdig.0000550. eCollection 2024 Aug.

Abstract

One of the most important tools available to limit the spread and impact of infectious diseases is vaccination. It is therefore important to understand what factors determine people's vaccination decisions. To this end, previous behavioural research made use of, (i) controlled but often abstract or hypothetical studies (e.g., vignettes) or, (ii) realistic but typically less flexible studies that make it difficult to understand individual decision processes (e.g., clinical trials). Combining the best of these approaches, we propose integrating real-world Bluetooth contacts via smartphones in several rounds of a game scenario, as a novel methodology to study vaccination decisions and disease spread. In our 12-week proof-of-concept study conducted with N = 494 students, we found that participants strongly responded to some of the information provided to them during or after each decision round, particularly those related to their individual health outcomes. In contrast, information related to others' decisions and outcomes (e.g., the number of vaccinated or infected individuals) appeared to be less important. We discuss the potential of this novel method and point to fruitful areas for future research.

摘要

限制传染病传播和影响的最重要工具之一是疫苗接种。因此,了解哪些因素决定人们的疫苗接种决策非常重要。为此,以往的行为研究采用了以下两种方法:(i)可控但往往抽象或假设性的研究(例如,情景描述),或者(ii)现实但通常灵活性较差的研究,这类研究难以理解个体决策过程(例如,临床试验)。结合这些方法的优点,我们建议通过智能手机在多轮游戏场景中整合现实世界中的蓝牙接触情况,作为研究疫苗接种决策和疾病传播的一种新方法。在我们对494名学生进行的为期12周的概念验证研究中,我们发现参与者对在每个决策轮次期间或之后提供给他们的一些信息有强烈反应,特别是那些与他们个人健康结果相关的信息。相比之下,与他人决策和结果相关的信息(例如,接种疫苗或感染的人数)似乎不太重要。我们讨论了这种新方法的潜力,并指出了未来研究的富有成果的领域。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/652b/11309433/23044260e92e/pdig.0000550.g001.jpg

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