Evidence map›Paper›PMID 34816254›Full record

ReviewComputational urban science2021

"What should be computed" for supporting post-pandemic recovery policymaking? A life-oriented perspective.

Junyi Zhang, Tao Feng, Jing Kang, Shuangjin Li, Rui Liu, Shuang Ma, Baoxin Zhai, Runsen Zhang, Hongxiang Ding, Taoxing Zhu

Abstract readReview
In one paragraph

Review in Computational urban science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

10 authors.

Junyi ZhangMobilities and Urban Policy Lab, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, Japan.ORCID 0000-0002-3267-542X
Tao FengMobilities and Urban Policy Lab, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, Japan.
Jing KangMobilities and Urban Policy Lab, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, Japan.
Shuangjin LiMobilities and Urban Policy Lab, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, Japan.
Rui LiuMobilities and Urban Policy Lab, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, Japan.
Shuang MaMobilities and Urban Policy Lab, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, Japan.
Baoxin ZhaiMobilities and Urban Policy Lab, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, Japan.
Runsen ZhangMobilities and Urban Policy Lab, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, Japan.
Hongxiang DingMobilities and Urban Policy Lab, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, Japan.
Taoxing ZhuMobilities and Urban Policy Lab, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic has caused various impacts on people's lives, while changes in people's lives have shown mixed effects on mitigating the spread of the SARS-CoV-2 virus. Understanding how to capture such two-way interactions is crucial, not only to control the pandemic but also to support post-pandemic urban recovery policies. As suggested by the life-oriented approach, the above interactions exist with respect to a variety of life domains, which form a complex behavior system. Through a review of the literature, this paper first points out inconsistent evidence about behavioral factors affecting the spread of COVID-19, and then argues that existing studies on the impacts of COVID-19 on people's lives have ignored behavioral co-changes in multiple life domains. Furthermore, selected uncertain trends of people's lives for the post-pandemic recovery are described. Finally, this paper concludes with a summary about "what should be computed?" in

Indexed as

Big dataCOVID-19 pandemicDaily lifeLife-oriented approachPolicymakingPost-pandemic recoveryQuestionnaire data

Identifiers

PMID34816254
PMCPMC8602982

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.