Evidence map›Paper›PMID 36232209›Full record

ArticleInternational journal of environmental research and public health2022

Research on Emergency Response Policy for Public Health Emergencies in China-Based on Content Analysis of Policy Text and PMC-Index Model.

Ying Zhao, Lin Wu

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–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

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  17. Achilles' heel: elderly COVID-19 vaccination policy in China.Health research policy and systems · 2024
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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

2 authors.

Ying ZhaoSchool of Sociology, Wuhan University, Wuhan 430072, China.ORCID 0000-0002-0699-4128
Lin WuSchool of Sociology, Wuhan University, Wuhan 430072, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Policy is an important support for risk society to prevent and resolve crises. Based on the content analysis of the policy text and PMC-Index model, this paper takes texts of 327 public health emergency response policies (PHERP) at the central level in China from 1989 to 2022 as the analysis object, designs an indicator system, and combines qualitative and quantitative methods to evaluate the existing policies. The results of content analysis indicate that current policy focuses on emergency rather than preventive control, the main policy-making and issuing authority is the Ministry of Health and policies are mostly issued in the form of notice. The PMC-Index of ten selected policies is all ranked above acceptable, which means that the overall quality of policy text is relatively high. However, the PMC-Surface shows that there is still considerable variability in the scores of the main indicators for each policy. The top three main scoring indicators are policy nature, policy evaluation and policy instrument, while the bottom three are policy time, policy release agency and policy target groups, which reminds us that the design of policy text can still be improved in terms of optimizing policy time, policy issuing institutions and expanding policy target groups. In response to these problems, this paper puts forward six suggestions for optimization.

Indexed as

EmergenciesPublic HealthChinaHealth PolicyHumansPolicy MakingPublic Policypolicy evaluationpublic health emergencypublic policyqualitativequantitative

Identifiers

PMID36232209
PMCPMC9566489

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.