Evidence map›Paper›PMID 36639781›Full record

ArticleBMC public health2023

COVID-19, stigma, and habituation: evidence from mobility data.

Kenichi Kurita, Yuya Katafuchi, Shunsuke Managi

Abstract read
In one paragraph

Article in BMC public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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

3 authors.

Kenichi KuritaDepartment of Environmental Changes, Faculty of Social and Cultural Studies, Kyushu University, Fukuoka, Japan. kurita.kenichi.564@m.kyushu-u.ac.jp.
Yuya KatafuchiResearch Institute for Humanity and Nature, Kyoto, Japan.
Shunsuke ManagiUrban Institute, Kyushu University, Fukuoka, Japan.

Funding

Japan Society for the Promotion of Science JP19K23194Japan Society for the Promotion of Science JP20H00648Japan Society for the Promotion of Science JP20K22142Japan Society for the Promotion of Science,Japan JP20K13486
6 · The paper itself

Abstract

backgroundThe Japanese government has restricted people's going-out behavior by declaring a non-punitive state of emergency several times under COVID-19. This study aims to analyze how multiple policy interventions that impose non-legally binding restrictions on behavior associate with people's going-out. THEORY: This study models the stigma model of self-restraint behavior under the pandemic with habituation effects. The theoretical result indicates that the state of emergency's self-restraint effects weaken with the number of times.

methodsThe empirical analysis examines the impact of emergency declarations on going-out behavior using a prefecture-level daily panel dataset. The dataset includes Google's going-out behavior data, the Japanese government's policy interventions based on emergency declarations, and covariates that affect going-out behavior, such as weather and holidays.

resultsFirst, for multiple emergency declarations from the beginning of the pandemic to 2021, the negative association between emergency declarations and mobility was confirmed in a model that did not distinguish the number of emergency declarations. Second, in the model that considers the number of declarations, the negative association was found to decrease with the number of declarations.

conclusionThese empirical analyses are consistent with the results of theoretical analyses, which show that the negative association between people's going-out behavior and emergency declarations decreases in magnitude as the number of declarations increases.

Indexed as

COVID-19GovernmentHabituation, PsychophysiologicHumansPandemicsSocial StigmaCOVID-19Infection diseaseMobility dataNon-pharmaceutical interventionSelf-restraint behaviorStigma

Identifiers

PMID36639781
PMCPMC9839212

What OpenQuestion holds

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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.