Evidence map›Paper›PMID 38169962›Full record

ArticlePatient preference and adherence2023

The Influencing Role of Cultural Values on Attitudes of the Chinese Public Towards Traditional Chinese Medicine (TCM) for the Control of COVID-19.

Huajian Zhao, Ruifen Zhang, Yi Chen

Abstract read
In one paragraph

Article in Patient preference and adherence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–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

12 citing papers in PubMed.

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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

3 authors.

Huajian ZhaoSchool of Communication, Soochow University, Suzhou, Jiangsu Province, 215127, People's Republic of China.
Ruifen ZhangSchool of Public Health, Soochow University, Suzhou, Jiangsu Province, 215127, People's Republic of China.
Yi ChenSchool of Communication, Soochow University, Suzhou, Jiangsu Province, 215127, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The use of traditional Chinese medicine (TCM) for disease prevention and healthcare has been strongly supported by the Chinese government in recent years, but public perception of TCM remains controversial or resistant, especially in preventing and treating COVID-19. It is therefore crucial to understand the factors that influence public attitudes. Methods: By applying a measure of attitudes towards TCM in epidemic prevention, this study conducted a nationwide survey (n=1192) on the relationships between attitudes towards TCM for the control of COVID-19 and trust, perceptions of risk, scientific literacy, policy endorsement, and cultural values. Results: The results of the study showed surprisingly that scientific literacy and risk perception did not dominate the Chinese public's attitudes towards TCM for the control of COVID-19. Meanwhile, egalitarianism, policy endorsement, trust in scientist and healthcare personnel played a positive role. Moreover, egalitarianism positively regulates the relationship between trust in scientist and attitudes towards TCM epidemic prevention and control, while collectivism negatively regulates the relationship of the preceding variables. Conclusion: All these findings suggest that attitudes towards the use of TCM are not based on rational cognition and logical reasoning, but are mainly influenced by emotional and cultural factors. This study will help researchers and practitioners understand the importance of cultural values, trust, policy endorsement, and attitudes towards TCM epidemic prevention and control to TCM's use in healthcare.

Indexed as

COVID-19cultural valuespolicy endorsementTCM

Identifiers

PMID38169962
PMCPMC10759415

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Registered trials

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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.