ArticlePloS one2022
Narratives and counter-narratives in religious responses to COVID-19: A computational text analysis.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 16 citations in OpenAlex.
- Discovering the Self: A Grounded Theory of Women's Recovery From Gender-Based Religious Harm.Journal of advanced nursing · 2026Article
- Authority, trust, and healthcare communication in a religious minority: the case of the Haredi community in Israel during COVID-19.BMC public health · 2026Article
- Examining media's coverage of COVID-19 vaccines and social media sentiments on vaccine manufacturers' stock prices.Frontiers in public health · 2024Article
- Religion as a social force in health: complexities and contradictions.BMJ (Clinical research ed.) · 2023Article
- Article
- Development of a Japanese version of the Awe Experience Scale (AWE-S): A structural topic modeling approach.F1000Research · 2023Article
- Article
- Aggressive measures, rising inequalities, and mass formation during the COVID-19 crisis: An overview and proposed way forward.Frontiers in public health · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Religious responses to COVID-19 as portrayed in a major news source raise the issue of conflict or cooperation between religious bodies and public health authorities. We compared articles in the New York Times relating to religion and COVID-19 with the COVID-19 statements posted on 63 faith-based organizations' web sites, and with the guidance documents published by the Centers for Disease Control and Prevention (CDC) and World Health Organization (WHO) specifically for religious bodies. We used computational text analysis to identify and compare sentiments and topics in the three bodies of text. Sentiment analysis showed consistent positive values for faith-based organizations' texts throughout the period. The initial negative sentiment of religion-COVID-19 coverage in the New York Times rose over the period and eventually converged with the consistently positive sentiment of faith-based documents. In our topic modelling analysis, rank order and regression analysis showed that topic prevalence was similar in the faith-based and public health sources, and both showed statistically significant differences from the New York Times. We conclude that there is evidence of both narratives and counter-narratives, and that these showed demonstrable shifts over time. Text analysis of public documents shows alignment of the interests of public health and religious bodies, which can be discerned for the benefit of communities if parties are trusted and religious messages are consistent with public health communications.
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.