Evidence map›Paper›PMID 37963149›Full record

ArticlePloS one2023

The impact of confirmed cases of COVID-19 on residents' traditional Chinese medicine health literacy: A survey from Gansu Province of China.

Ge Zhu, Xiulan Wang, Tengyue Zhang, Wangping Zhao, Li Ma

Abstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

5 authors.

Ge ZhuSchool of Economics, Trade and Management, Gansu University of Chinese Medicine, Lanzhou, Gansu, China.
Xiulan WangSchool of Economics, Trade and Management, Gansu University of Chinese Medicine, Lanzhou, Gansu, China.ORCID 0000-0002-7634-7591
Tengyue ZhangSchool of Public Health, Gansu University of Chinese Medicine, Lanzhou, Gansu, China.
Wangping ZhaoSchool of Public Health, Gansu University of Chinese Medicine, Lanzhou, Gansu, China.
Li MaSchool of Traditional Chinese and Western Medicine, Gansu University of Chinese Medicine, Lanzhou, Gansu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since the outbreak of the new crown epidemic in China in early 2020, the number of confirmed cases of COVID-19 has continued to increase, and the Chinese government's policy of "static management" in the first round of the epidemic may affect the health behavior adjustment of Chinese residents. Using survey data on the TCM health literacy of 4016 residents in China (Gansu Province), a causal inference approach was used to explore the impact of the emergence of confirmed cases of COVID-19 on residents' TCM health literacy. We found that the emergence of confirmed cases can increase by 3.5%-7.0% in residents' TCM health literacy. Among them, the TCM health literacy of uneducated residents has not improved significantly, and the residents with secondary education have increased significantly by 8%. For those with higher education, the number of residents increased significantly by 6%. At the same time, the emergence of confirmed cases will increase the residents' practical TCM health literacy and decrease theoretical TCM health literacy. Through heterogeneity analysis, we explored the impact mechanism of confirmed cases on residents' TCM health literacy. We believe that the emergence of confirmed cases will make residents more inclined to participate in TCM-free clinics, theme activities, and other ways to acquire TCM knowledge.

Indexed as

COVID-19Health LiteracyChinaHealth BehaviorHumansMedicine, Chinese Traditional

Identifiers

PMID37963149
PMCPMC10645358

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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.