Evidence map›Paper›PMID 36913768›Full record

ArticleJournal of infection and public health2023

Predictors and moderators of COVID-19 pandemic fatigue in Hong Kong.

Daniel W L Lai, Jiahui Jin, Elsie Yan, Vincent W P Lee

Abstract read
In one paragraph

Article in Journal of infection and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  8. Heterogeneous risk tolerance, in-groups, and epidemic waves.Frontiers in applied mathematics and statistics · 2024
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4 · The record

Corrections and comments

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

4 authors.

Daniel W L LaiFaculty of Social Sciences, Hong Kong Baptist University, Hong Kong Special Administrative Region. Electronic address: daniel_lai@hkbu.edu.hk.
Jiahui JinWee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore.
Elsie YanDepartment of Applied Social Sciences, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region.
Vincent W P LeeFaculty of Social Sciences, Hong Kong Baptist University, Hong Kong Special Administrative Region.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAnti-pandemic fatigue has inevitably set in owing to the high intensity and prolonged presence of pandemic preventive measures. Globally, COVID-19 remains severe; however, pandemic fatigue may lead to less efficient viral control.

methodsA total of 803 participants residing in Hong Kong interviewed via telephone using a structured questionnaire. Linear regression was employed to test the corelates of anti-pandemic fatigue and the moderators that could potentially impact the appearance of fatigue.

resultsWhen confounding effects of demographic factors (e.g., age, gender, educational attainment, and economic activity status) were avoided, daily hassles were found to be a core factor associated with anti-pandemic fatigue (B =0.369, SE =0.049, p = 0.000). For people with a higher level of pandemic-related knowledge and fewer obstacles brought about by preventive measures, the impact of daily hassles on pandemic fatigue weakened. Moreover, when pandemic-related knowledge was high, there was no positive association between adherence and fatigue.

conclusionsThis study confirms that daily hassles can lead to anti-pandemic fatigue, which can be mitigated by improving the general public's understanding of the virus and developing more convenient measures.

Indexed as

COVID-19FatigueHong KongHumansPandemicsSurveys and QuestionnairesAnti-pandemic fatigueCOVID-19Hassles

Identifiers

PMID36913768
PMCPMC9990889

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.