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Record W3084096474 · doi:10.3389/fpsyt.2020.569981

The Association Between Physical and Mental Health and Face Mask Use During the COVID-19 Pandemic: A Comparison of Two Countries With Different Views and Practices

2020· article· en· W3084096474 on OpenAlexaff
Cuiyan Wang, Agata Chudzicka‐Czupała, Damian Grabowski, Katarzyna Adamus, Xiaoyang Wan, Mateusz Hetnał, Yilin Tan, Agnieszka Olszewska-Guizzo, Linkang Xu, Roger S. McIntyre, Jessica Quek, Roger Ho, Cyrus S. H. Ho

Bibliographic record

VenueFrontiers in Psychiatry · 2020
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersHuaibei Normal UniversityNational University of Singapore
KeywordsMental healthAnxietyDepression (economics)PandemicBivariate analysisMedicineChinaDemographyPsychologyCoronavirus disease 2019 (COVID-19)PsychiatryClinical psychologyGeographyDisease

Abstract

fetched live from OpenAlex

BACKGROUND: The physical and mental health of citizens living in a country that encouraged face masks (China) and discouraged face masks (Poland) during the initial stage of the COVID-19 pandemic remained unknown. We conducted a cross-country study to compare the psychological impact of the COVID-19 pandemic on Poles and Chinese. This study aimed to compare the levels of psychological impact of pandemic and levels of anxiety and depression between China and Poland. METHODS: The survey collected information on demographic data, physical symptoms, contact history, and precautionary measures. The psychological impact was assessed using the Impact of Event Scale-Revised (IES-R), and mental health status was assessed by the Depression, Anxiety and Stress Scale (DASS-21). The chi-squared test was used to analyze the differences in categorical variables between the two populations. Linear regression was used to calculate the bivariate associations between independents variables (e.g., physical symptoms and precautionary measures) and dependent variables (e.g., mental health outcomes). RESULTS: This study included a total of 2,266 respondents from both countries (1,056 Poles and 1,210 Chinese). There were significantly less Polish respondents who wore face masks (Poles: 35.0%; Chinese: 96.8% p < 0.001). Significantly more Polish respondents reported physical symptoms resembling COVID-19 infection (p < 0.001), recent medical consultation (p < 0.01), recent COVID-19 testing (p < 0.001), and hospitalization (p < 0.01). Furthermore, Polish respondents had significantly higher levels of anxiety, depression and stress (p < 0.001) than Chinese. The mean IES-R scores of Poland and China were above the cut-off for post-traumatic stress disorder (PTSD) symptoms. Besides precautionary measures, unemployment, retirement, physical symptoms resembling COVID-19 infection, recent medical consultation or COVID-19 testing, and long daily duration of home confinement were risk factors for PTSD symptoms, anxiety, depression, or stress for Polish respondents. CONCLUSION: Use of face masks at the community level may safeguard better physical and mental health during the COVID-19 pandemic. There is a need of health education with scientific information from Polish health authority on the proper use of face masks and reduce social stigma. This study was limited by the respondent sampling method that had compromised the representativeness of samples.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.083
GPT teacher head0.435
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations329
Published2020
Admission routes1
Has abstractyes

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