Evidence map›Paper›PMID 33333329›Full record

ArticleThe Science of the total environment2021

Coronavirus disease 2019 (COVID-19) and psychological distress in China: Does neighbourhood matter?

Yiyi Chen, Colin Jones, Neil Dunse

Open access · greenAbstract read
In one paragraph

Article in The Science of the total environment, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 2 pooled it
4.4field-weighted citation impact, top 6% 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

7 citing papers in PubMed, 2 syntheses or guidelines pooled it, 18 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Contributors to Neighbour Noise Annoyance.International journal of environmental research and public health · 2021
    Article
  7. 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

3 authors at 1 institution in 1 country.

Yiyi ChenThe Urban Institute, Heriot-Watt University, Edinburgh EH14 4AS, UK. Electronic address: Yc50@hw.ac.uk.
Colin JonesThe Urban Institute, Heriot-Watt University, Edinburgh EH14 4AS, UK. Electronic address: C.a.jones@hw.ac.uk.
Neil DunseThe Urban Institute, Heriot-Watt University, Edinburgh EH14 4AS, UK. Electronic address: N.A.Dunse@hw.ac.uk.
Heriot-Watt University · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Using individual data (n = 937) obtained from an online questionnaire between 20th January and 13th February, the early stage of the outbreak of the Coronavirus Disease 2019 (COVID-19) in 2020, we explore the direct association between people's perceptions of Coronavirus Disease 2019 (COVID-19) and psychological distress. We further examine the moderating role of neighbourhood environment and this distress. We find that people living in infected communities tend to perceive higher level of psychological distress compared to people living in uninfected communities. People's expected duration of COVID-19 is associated with higher psychological distress and this is partially moderated by the perception of neighbourhood noise level and overall environment quality. Additional results quantify the evidence that a quiet and well maintained neighbourhood environment could reduce the negative influences of expectation of a long duration of COVID-19 on people's psychological distress.

Indexed as

COVID-19ChinaDisease OutbreaksHumansPsychological DistressSARS-CoV-2Stress, PsychologicalCoronavirus disease 2019 (COVID-19)Kessler psychological distress scaleNeighbourhood environmentPsychological distress

Identifiers

PMID33333329
PMCPMC7832354
OpenAlexW3107160015

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.