Evidence map›Paper›PMID 38932777›Full record

ArticleFrontiers in public health2024

Exploring the Singapore general population's trust in COVID-19 information from different sources and its association with perceived risk of infection during the pandemic.

Fiona Devi, Bernard Chin Wee Tan, Saleha Shafie, Yun Jue Zhang, Shazana Shahwan, Pratika Satghare, Siow Ann Chong, Mythily Subramaniam

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Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Fiona DeviResearch Division, Institute of Mental Health, Singapore, Singapore.
Bernard Chin Wee TanResearch Division, Institute of Mental Health, Singapore, Singapore.
Saleha ShafieResearch Division, Institute of Mental Health, Singapore, Singapore.
Yun Jue ZhangResearch Division, Institute of Mental Health, Singapore, Singapore.
Shazana ShahwanResearch Division, Institute of Mental Health, Singapore, Singapore.
Pratika SatghareResearch Division, Institute of Mental Health, Singapore, Singapore.
Siow Ann ChongResearch Division, Institute of Mental Health, Singapore, Singapore.
Mythily SubramaniamResearch Division, Institute of Mental Health, Singapore, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The degree of public trust in the government's competence is crucial in preventing the spread of misinformation and reducing psychological distress during a pandemic. The study aimed to (i) explore the trust in COVID-19 information from different sources and trust in the ability of the World Health Organization (WHO), government departments and related institutions in handling the epidemic in Singapore and (ii) its association with perceived risk of infection among Singapore residents. Methods: A total of 1,129 participants (aged 21 and above) were recruited from a cross-sectional study examining the well-being and resilience of Singapore's population between May 2020 to June 2021. Trust in COVID-19 information from different sources was measured on a 10-point scale and an ordinal 7-point scale was used for perceived risk of infection. Descriptive statistics and multivariate logistic regression model were conducted. Results: 85.5% reported high trust in COVID-19 information from the government and their ability in handling the pandemic. Participants also reported high trust in COVID-19 information from local public health or infectious disease experts (84.4%) and traditional media (77.2%). Low trust in the ability of government departments and related institutions was associated with higher future (1 month) perceived risk of infection (OR: 5.7, 95% CI 1.02-32.45) and low trust in social media was associated with higher current perceived risk of infection (OR: 2.4, 95% CI 1.09-5.24). Discussion: The present study provided insight on the level of trust on COVID-19 information from different sources and its associated perceived risks of infection. Future qualitative studies are recommended to facilitate better understanding of public trust and identify strategies for how it can be effectively addressed to support future public health responses.

Indexed as

COVID-19TrustAdultAgedCross-Sectional StudiesFemaleHumansMaleMiddle AgedPandemicsSARS-CoV-2SingaporeSurveys and QuestionnairesYoung AdultAsiaCOVID-19multi-ethnicperceived risks of infectiontrust

Identifiers

PMID38932777
PMCPMC11204297

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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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.