Evidence map›Paper›PMID 35969978›Full record

ArticleSocial science & medicine (1982)2022

Why they willingly complied: Ordinary people, the big environment, and the control of COVID-19 in China.

Yifeng Troy Cai, Katherine A Mason

Open access · greenAbstract read
In one paragraph

Article in Social science & medicine (1982), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
17.5field-weighted citation impact, top 1% of its field
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

8 citing papers in PubMed, 21 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. The Potential Mechanisms of Arrhythmia in Coronavirus disease-2019.International journal of medical sciences · 2024
    Review
  7. Article
  8. 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

2 authors at 1 institution in 1 country.

Yifeng Troy CaiDepartment of Anthropology, Brown University, Box 1921, Providence, RI 02912, USA; School of Public Health, Brown University, 121 S Main St, Providence, RI 02903, USA.
Katherine A MasonDepartment of Anthropology, Brown University, Box 1921, Providence, RI 02912, USA. Electronic address: katherine_mason@brown.edu.
Brown University · US

Funding

Science/Technical CoreP2CHD041020 · NICHD · BROWN UNIVERSITY · PI ZHENCHAO QIAN · 2016 to 2026
$7.6M
NICHD NIH HHS P2C HD041020
6 · The paper itself

Abstract

During the first two years of the COVID-19 pandemic, the Chinese public consistently demonstrated a high level of compliance with some of the most restrictive infection control measures in the world. As a result, as of early 2022 China achieved remarkable control of a virus that had devastating effects in other parts of the world. In this article we take seriously the complexities of a simple question: Why did most urban Chinese citizens so willingly comply with the state's COVID-19 control measures for so long? Based on two years of ethnographic research conducted primarily in Shanghai, China between June 2020 and May 2022, we argue that the strong support the Chinese government enjoyed among China's self-described laobaixing ("ordinary people") in implementing its COVID-19 control measures emerged from a combination of self-interest, nationalistic pride, and "conscious indifference to transparency," rooted in ongoing critical evaluations of governmental competence. With these evaluations changing in the wake of new outbreaks in 2022, the future of China's zero-COVID policy is in jeopardy.

Indexed as

COVID-19ChinaDisease OutbreaksHumansPandemicsChinaComplianceCOVID-19LockdownsNationalismSelf-interestTransparencyTrust

Identifiers

PMID35969978
PMCPMC9356634
OpenAlexW4292399426

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.