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预测数字接触者追踪接受度的前置因素:系统回顾和荟萃分析。

Antecedents predicting digital contact tracing acceptance: a systematic review and meta-analysis.

机构信息

Department of Business Management, National United University, No.1, 360301, Lienda, Miaoli, Taiwan, Republic of China.

出版信息

BMC Med Inform Decis Mak. 2023 Oct 11;23(1):212. doi: 10.1186/s12911-023-02313-1.


DOI:10.1186/s12911-023-02313-1
PMID:37821864
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10568897/
Abstract

An awareness of antecedents of acceptance of digital contact tracing (DCT) can enable healthcare authorities to design appropriate strategies for fighting COVID-19 or other infectious diseases that may emerge in the future. However, mixed results about these antecedents are frequently reported. Most prior DCT acceptance review studies lack statistical synthesis of their results. This study aims to undertake a systematic review and meta-analysis of antecedents of DCT acceptance and investigate potential moderators of these antecedents. By searching multiple databases and filtering studies by using both inclusion and exclusion criteria, 76 and 25 studies were included for systematic review and meta-analysis, respectively. Random-effects models were chosen to estimate meta-analysis results since Q, I , and H index signified some degree of heterogeneity. Fail-safe N was used to assess publication bias. Most DCT acceptance studies have focused on DCT related factors. Included antecedents are all significant predictors of DCT acceptance except for privacy concerns and fear of COVID-19. Subgroup analysis showed that individualism/collectivism moderate the relationships between norms/privacy concerns and intention to use DCT. Based on the results, the mean effect size of antecedents of DCT acceptance and the potential moderators may be more clearly identified. Appropriate strategies for boosting the DCT acceptance rate can be proposed accordingly.

摘要

对数字接触追踪(DCT)接受的前因的认识,可以使医疗保健当局能够设计出针对抗击 COVID-19 或未来可能出现的其他传染病的适当策略。然而,这些前因的结果往往是混合的。大多数先前的 DCT 接受度审查研究缺乏对其结果的统计综合。本研究旨在对 DCT 接受度的前因进行系统评价和荟萃分析,并研究这些前因的潜在调节因素。通过搜索多个数据库并使用纳入和排除标准对研究进行筛选,分别有 76 项和 25 项研究被纳入系统评价和荟萃分析。由于 Q、I 和 H 指数表示存在一定程度的异质性,因此选择了随机效应模型来估计荟萃分析结果。失效安全 N 用于评估发表偏倚。大多数 DCT 接受研究都集中在 DCT 相关因素上。除了隐私问题和对 COVID-19 的恐惧外,纳入的前因都是 DCT 接受度的显著预测因素。亚组分析表明,个人主义/集体主义调节了规范/隐私问题与使用 DCT 的意愿之间的关系。根据研究结果,可以更清楚地确定 DCT 接受度的前因和潜在调节因素的平均效应大小。可以相应地提出提高 DCT 接受率的适当策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be92/10568897/350d7424786e/12911_2023_2313_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be92/10568897/e2468a5db3b8/12911_2023_2313_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be92/10568897/659c3b677167/12911_2023_2313_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be92/10568897/fa20dd237122/12911_2023_2313_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be92/10568897/6f6f414ca841/12911_2023_2313_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be92/10568897/b64fe28f1d8b/12911_2023_2313_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be92/10568897/b00c05db4d16/12911_2023_2313_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be92/10568897/350d7424786e/12911_2023_2313_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be92/10568897/e2468a5db3b8/12911_2023_2313_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be92/10568897/659c3b677167/12911_2023_2313_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be92/10568897/fa20dd237122/12911_2023_2313_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be92/10568897/6f6f414ca841/12911_2023_2313_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be92/10568897/b64fe28f1d8b/12911_2023_2313_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be92/10568897/b00c05db4d16/12911_2023_2313_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be92/10568897/350d7424786e/12911_2023_2313_Fig7_HTML.jpg

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Antecedents predicting digital contact tracing acceptance: a systematic review and meta-analysis.

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本文引用的文献

[1]
Predicting the use of a COVID-19 contact tracing application: A study across two points of measurements.

Appl Psychol Health Well Being. 2023-11

[2]
A privacy calculus model for contact tracing apps: Analyzing the use behavior of the German Corona-Warn-App with a longitudinal user study.

Comput Secur. 2023-9

[3]
Why Individuals Do (Not) Use Contact Tracing Apps: A Health Belief Model Perspective on the German Corona-Warn-App.

Healthcare (Basel). 2023-2-15

[4]
Explaining citizens' resistance to use digital contact tracing apps: A mixed-methods study.

Int J Inf Manage. 2022-4

[5]
To disclose or to falsify: The effects of cognitive trust and affective trust on customer cooperation in contact tracing.

Int J Hosp Manag. 2021-4

[6]
The impact of perceived crisis severity on intention to use voluntary proximity tracing applications.

Int J Inf Manage. 2021-12

[7]
Antecedent, behaviour, and consequence (a-b-c) of deploying the contact tracing app in response to COVID-19: Evidence from Europe.

Technol Forecast Soc Change. 2023-2

[8]
Adherence to emotion norms is greater in individualist cultures than in collectivist cultures.

J Pers Soc Psychol. 2023-6

[9]
Continued Use of Contact-Tracing Apps in the United States and the United Kingdom: Insights From a Comparative Study Through the Lens of the Health Belief Model.

JMIR Form Res. 2022-12-8

[10]
The relationship between trust and attitudes towards the COVID-19 digital contact-tracing app in the UK.

PLoS One. 2022

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