Evidence map›Paper›PMID 35186836›Full record

ArticleFrontiers in public health

Investigating the Adoption of Precautionary Behaviors Among Young Rural Adults in South Iran During COVID-19.

Maryam Tajeri Moghadam, Tahereh Zobeidi, Stefan Sieber, Katharina Löhr

Abstract read
In one paragraph

Article in Frontiers in public health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 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

4 authors.

Maryam Tajeri MoghadamDepartment of Extension and Rural Development, Faculty of Agriculture, University of Tabriz, Tabriz, Iran.
Tahereh ZobeidiDepartment of Agricultural Extension, Communication and Rural Development, Faculty of Agriculture, University of Zanjan, Zanjan, Iran.
Stefan SieberLeibniz Center for Agricultural Landscape Research (ZALF), Müncheberg, Germany.
Katharina LöhrDepartment of Extension and Rural Development, Faculty of Agriculture, University of Tabriz, Tabriz, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 is an unprecedented challenge for public health worldwide. Reducing the incidence of the disease requires protective measures to prevent virus transmission. Understanding those factors influencing preventive behavior is the first step in preventing the spread of the disease. This study investigates factors affecting youth intention and preventive behaviors in the face of COVID-19 through the health belief model by using a cross-sectional survey collected through an online questionnaire. The sample comprises 304 rural youth in South Iran who were selected through a random sampling technique. The results reveal that perceived severity, perceived benefits, public health beliefs, perceived self-efficacy, and the cue to act positively and significantly affect preventive behaviors. The model explains 59% of variance changes in rural youth preventive behaviors during COVID-19. Cue to action is the strongest and self-efficacy was the weakest determinant of youth's preventive behavior. This study confirms that the HBM framework has appropriate predictive power and is an effective tool for investigating preventive behaviors during COVID-19. These results provide important policy implications for the development of policies that aim to avoid the further spread of COVID-19 between young citizens.

Indexed as

COVID-19AdolescentAdultCross-Sectional StudiesHumansIranRural PopulationSARS-CoV-2adoptionCOVID-19health belief modelpreventive behaviorrural youth

Identifiers

PMID35186836
PMCPMC8847444

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.