Evidence map›Paper›PMID 34539039›Full record

ArticleInformation processing & management2021

Information avoidance during health crises: Predictors of avoiding information about the COVID-19 pandemic among german news consumers.

Elena Link

Abstract readNews
In one paragraph

Article in Information processing & management, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

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  19. Information avoidance in the age of COVID-19: A meta-analysis.Information processing & management · 2023
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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

1 author.

Elena LinkDepartment of Journalism and Communication Research, University of Music, Drama and Media, Hanover, Expo Plaza 12, 30539 Hanover, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study investigates the prevalence of source-specific information avoidance among German consumers and predictors of information-avoidance behavior in the context of the COVID-19 pandemic. Guided by the Risk Information Seeking and Processing Model (RISP), we propose that the perceived social norms, information insufficiency, risk perception, affective risk response, and attitudes toward seeking predict information avoidance. We supplement the RISP model by considering information overload as the vast volume and the incredulous quality of information in the COVID-19 pandemic challenge individuals' information acquisition and processing. Using a stratified demographic sample of news consumers of a federal German state (N = 1,000), we empirically examined the proposed model, answered the research question, and tested our hypotheses using structural equation modeling. The results reveal information avoidance among one-third of the respondents. They avoided online sources, including online-mediated interpersonal sources, more often than interpersonal sources and traditional mass media. Information avoidance was linked to more negative attitudes toward seeking and negative affective risk responses, more pronounced descriptive and injunctive avoidance norms, and perceived information overload. Attitudes and information overload were the most influential predictors of avoidance. In contrast, risk perception and information insufficiency were not associated with information avoidance. This study provides insights into theory development, contributes to the information behavior literature, and identifies barriers to communication during health crises.

Indexed as

COVID-19Information avoidanceInformation insufficiencyRisk information seeking and processing modelRisk perceptionSocial norms

Identifiers

PMID34539039
PMCPMC8441302

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.