Evidence map›Paper›PMID 36471303›Full record

ArticleBMC public health2022

Explicating gender disparity in wearing face masks during the COVID-19 pandemic.

Kim Hoe Looi

Open access · goldAbstract read
In one paragraph

Article in BMC public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
4.1field-weighted citation impact, top 6% of its field
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

8 citing papers in PubMed, 15 citations in OpenAlex.

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  8. Sex and Gender Differences in Tuberculosis Pathogenesis and Treatment Outcomes.Current topics in microbiology and immunology · 2023
    Review
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

1 author at 1 institution in 1 country.

Kim Hoe LooiXiamen University Malaysia, Bandar Sunsuria, Malaysia. gideon_looi@hotmail.com.
Xiamen University Malaysia · MY

Funding

Xiamen University Malaysia XMUMRF/2019-C4/ISEM/0022]
6 · The paper itself

Abstract

backgroundThe available evidence suggests that women were more likely to wear face masks as a precaution during the COVID-19 pandemic. However, few studies have explicated this gender disparity in wearing face masks. This study investigates associations of demographic factors with wearing face masks in Malaysia during the COVID-19 pandemic, then explicates gender disparity in wearing face masks from the lens of the Protection Motivation Theory.

methodsThe first part of this study employed a structured online survey of 708 Malaysian adult participants. Data collected were quantitatively analyzed by means of descriptive statistics, bivariate correlations, analysis of variance (ANOVA), and multiple linear regression. The second part of this study was conducted among 28 women to better understand gender disparity in protection motivations from the perspectives of women.

resultsGender has the strongest positive association with wearing face masks (p-value < .001), followed by age (p-value = .028). The Protection Motivation Theory adequately explicated the gender disparity in wearing face masks. Additionally, women were motivated to wear face masks beyond protection from the SARS-CoV-2.

conclusionUnderstanding the underlying motivations for wearing face masks informs design of gender-based public health messages to increase compliance with public health regulations and reduce morbidity and mortality for present and future public health crises.

Indexed as

COVID-19AdultFemaleHumansMasksPandemicsPublic HealthSARS-CoV-2COVID-19 pandemicGender-based public health messagesGender disparity in wearing face masksPersonal hygieneWearing face masks

Identifiers

PMID36471303
PMCPMC9724360
OpenAlexW4311018493

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.