How to Put It Plainly? Findings From Two Randomized Controlled Studies on Writing Plain Language Summaries for Psychological Meta-Analyses.

accessibility empowerment guideline knowledge acquisition lay summaries plain language summaries science communication understanding

Journal

Frontiers in psychology
ISSN: 1664-1078
Titre abrégé: Front Psychol
Pays: Switzerland
ID NLM: 101550902

Informations de publication

Date de publication:
2021
Historique:
received: 06 09 2021
accepted: 08 11 2021
entrez: 3 1 2022
pubmed: 4 1 2022
medline: 4 1 2022
Statut: epublish

Résumé

Plain language summaries (PLS) aim to communicate research findings to laypersons in an easily understandable manner. Despite the societal relevance of making psychological research findings available to the public, our empirical knowledge on how to write PLS of psychology studies is still scarce. In this article, we present two experimental studies investigating six characteristics of PLS for psychological meta-analyses. We specifically focused on approaches for (1) handling technical terms, (2) communicating the quality of evidence by explaining the methodological approach of meta-analyses, (3) explaining how synthesized studies operationalized their research questions, (4) handling statistical terms, (5) structuring PLS, and (6) explaining complex meta-analytic designs. To develop empirically validated guidelines on writing PLS, two randomized controlled studies including large samples stratified for education status, age, and gender (

Identifiants

pubmed: 34975663
doi: 10.3389/fpsyg.2021.771399
pmc: PMC8717946
doi:

Types de publication

Journal Article

Langues

eng

Pagination

771399

Informations de copyright

Copyright © 2021 Kerwer, Stoll, Jonas, Benz and Chasiotis.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a conflict of interest.

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Auteurs

Martin Kerwer (M)

Leibniz Institute for Psychology (ZPID), Trier, Germany.

Marlene Stoll (M)

Leibniz Institute for Psychology (ZPID), Trier, Germany.
Leibniz Institute for Resilience Research (LIR), Mainz, Germany.

Mark Jonas (M)

Leibniz Institute for Psychology (ZPID), Trier, Germany.

Gesa Benz (G)

Leibniz Institute for Psychology (ZPID), Trier, Germany.

Anita Chasiotis (A)

Leibniz Institute for Psychology (ZPID), Trier, Germany.

Classifications MeSH