ArticleBMC public health2023
Why elderly in rural China didn't wear masks during the COVID-19 pandemic? A qualitative narrative interview study.
Article in BMC public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed.
- Rural residents' cognition of healthy lifestyles: a qualitative study in Shanxi Province, China.Scientific reports · 2026Article
- Evaluation of the effectiveness of rehabilitation nursing training for clinical nurses based on the Kirkpatrick model.BMC nursing · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDuring the COVID-19 pandemic, ageism and stigmatization towards the elderly have been prominent issues. In addition, there have been debates on Chinese social media as to why elderly people in rural areas are not wearing masks. While some factors that affect the mask-wearing behaviour of the elderly have been analyzed, little attention has been given to the lived experiences and behavioral intentions of rural elderly people who choose not to wear masks, despite government mandates to do so.
methodIn this research, 50 semi-structured interviews with 30 elderly individuals in three Chinese villages were carried out using the qualitative method of semi-structured interviews. Following verbatim recording and transcription of the conversations, the subject was analyzed using the Theory of Reasoned Action.
resultsWe identified four factors that influence the non-masking behaviour of rural elderly, including past experiences, cultural concepts, cognitive attitudes, and health and safety anxiety, and identified nine sub-themes based on the four overarching themes. Past knowledge, experience, and history have led rural elderly people to distrust the government's mandatory "mask mandate," believing that they do not need to wear masks. Rural cultural concepts and habits make the elderly feel that masks not only fail to provide protection but also become obstacles, resulting in poor daily experiences. Cognitive attitudes and emotions determine the elderly's evaluation of masks, which in turn affects their use of masks. Finally, elderly individuals'chronic diseases directly affect the physical pain and life safety caused by their use of masks, which is a major objective factor for their non-masking.
conclusionsAlthough numerous studies have concluded that Chinese people wore masks out of collectivism and conformity during the pandemic, marginalized groups' opposition to wearing masks also contains distinctive, individualized elements and underlying causes. By exposing these elements and reasons, we can better comprehend the peculiar behavior of particular groups while fighting the pandemic. The needs of marginalized populations should be prioritized by public health policy makers to provide more equitable services.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.