Evidence map›Paper›PMID 33020395›Full record

ArticleInternational journal of environmental research and public health2020

How to Improve Compliance with Protective Health Measures during the COVID-19 Outbreak: Testing a Moderated Mediation Model and Machine Learning Algorithms.

Paolo Roma, Merylin Monaro, Laura Muzi, Marco Colasanti, Eleonora Ricci, Silvia Biondi, Christian Napoli, Stefano Ferracuti, Cristina Mazza

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.

0numbers the graph read from it
0cells of the map it votes in
61citing 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

61 citing papers in PubMed.

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  2. Thinking of future as an older individual increases perceived risks for age-related diseases but not for COVID-19.International journal of psychology : Journal international de psychologie · 2022
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1 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Paolo RomaDepartment of Human Neuroscience, Sapienza University of Rome, 00185 Rome, Italy.ORCID 0000-0002-1031-0948
Merylin MonaroDepartment of General Psychology, University of Padova, 35131 Padova, Italy.
Laura MuziDepartment of Dynamic and Clinical Psychology, Sapienza University of Rome, 00185 Rome, Italy.
Marco ColasantiDepartment of Human Neuroscience, Sapienza University of Rome, 00185 Rome, Italy.
Eleonora RicciDepartment of Human Neuroscience, Sapienza University of Rome, 00185 Rome, Italy.
Silvia BiondiDepartment of Human Neuroscience, Sapienza University of Rome, 00185 Rome, Italy.
Christian NapoliDepartment of Medical Surgical Science and Translational Medicine, Sapienza University of Rome, 00189 Rome, Italy.ORCID 0000-0002-5775-2276
Stefano FerracutiDepartment of Human Neuroscience, Sapienza University of Rome, 00185 Rome, Italy.ORCID 0000-0003-1150-1460
Cristina MazzaDepartment of Neuroscience, Imaging and Clinical Sciences, University "G.d'Annunzio", 66100 Chieti-Pescara, Italy.ORCID 0000-0002-2554-8094

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the wake of the sudden spread of COVID-19, a large amount of the Italian population practiced incongruous behaviors with the protective health measures. The present study aimed at examining psychological and psychosocial variables that could predict behavioral compliance. An online survey was administered from 18-22 March 2020 to 2766 participants. Paired sample

Indexed as

Machine LearningAlgorithmsBetacoronavirusCommunicable Disease ControlCoronavirus InfectionsCOVID-19HumansItalyPandemicsPatient CompliancePneumonia, ViralSARS-CoV-2civic engagementcomplianceCOVID-19efficacypersonalityrisk perception

Identifiers

PMID33020395
PMCPMC7579153

What OpenQuestion holds

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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.