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Diabetes in the News: Readability Analysis of Malaysian Diabetes Corpus

This paper describes a study to evaluate the readability scores of Malaysian newspaper articles meant to create awareness of diabetes among the public. In contrast to patient-specific sources of information, mass media may potentially reach healthy people, thus preventing them from becoming part of...

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Autores principales: Hamat, Afendi, Jaludin, Azhar, Mohd-Dom, Tuti Ningseh, Rani, Haslina, Jamil, Nor Aini, Abdul Aziz, Aznida Firzah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9180217/
https://www.ncbi.nlm.nih.gov/pubmed/35682387
http://dx.doi.org/10.3390/ijerph19116802
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author Hamat, Afendi
Jaludin, Azhar
Mohd-Dom, Tuti Ningseh
Rani, Haslina
Jamil, Nor Aini
Abdul Aziz, Aznida Firzah
author_facet Hamat, Afendi
Jaludin, Azhar
Mohd-Dom, Tuti Ningseh
Rani, Haslina
Jamil, Nor Aini
Abdul Aziz, Aznida Firzah
author_sort Hamat, Afendi
collection PubMed
description This paper describes a study to evaluate the readability scores of Malaysian newspaper articles meant to create awareness of diabetes among the public. In contrast to patient-specific sources of information, mass media may potentially reach healthy people, thus preventing them from becoming part of the diabetes statistics. Articles published within a selected corpus from the years 2013 to 2018 and related to awareness regarding diabetes were sampled, and their readability was scored using Flesch Kinkaid Reading Ease (FKRE). Features of three articles ranked as the best and worst for readability were qualitatively analyzed. The average readability for the materials is low at 49.6 FKRE, which may impede the uptake of information contained in the articles. Feature analysis of articles with the best and worst readability indicates that medical practitioners may not be the best spokesperson to reach the public. It also indicates that simple sentence structures could help improve readability. There is still much room for improvement in attaining good public health literacy through mass media communication. Public health and media practitioners should be vigilant of the language aspects of their writing when reaching out to the public.
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spelling pubmed-91802172022-06-10 Diabetes in the News: Readability Analysis of Malaysian Diabetes Corpus Hamat, Afendi Jaludin, Azhar Mohd-Dom, Tuti Ningseh Rani, Haslina Jamil, Nor Aini Abdul Aziz, Aznida Firzah Int J Environ Res Public Health Article This paper describes a study to evaluate the readability scores of Malaysian newspaper articles meant to create awareness of diabetes among the public. In contrast to patient-specific sources of information, mass media may potentially reach healthy people, thus preventing them from becoming part of the diabetes statistics. Articles published within a selected corpus from the years 2013 to 2018 and related to awareness regarding diabetes were sampled, and their readability was scored using Flesch Kinkaid Reading Ease (FKRE). Features of three articles ranked as the best and worst for readability were qualitatively analyzed. The average readability for the materials is low at 49.6 FKRE, which may impede the uptake of information contained in the articles. Feature analysis of articles with the best and worst readability indicates that medical practitioners may not be the best spokesperson to reach the public. It also indicates that simple sentence structures could help improve readability. There is still much room for improvement in attaining good public health literacy through mass media communication. Public health and media practitioners should be vigilant of the language aspects of their writing when reaching out to the public. MDPI 2022-06-02 /pmc/articles/PMC9180217/ /pubmed/35682387 http://dx.doi.org/10.3390/ijerph19116802 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hamat, Afendi
Jaludin, Azhar
Mohd-Dom, Tuti Ningseh
Rani, Haslina
Jamil, Nor Aini
Abdul Aziz, Aznida Firzah
Diabetes in the News: Readability Analysis of Malaysian Diabetes Corpus
title Diabetes in the News: Readability Analysis of Malaysian Diabetes Corpus
title_full Diabetes in the News: Readability Analysis of Malaysian Diabetes Corpus
title_fullStr Diabetes in the News: Readability Analysis of Malaysian Diabetes Corpus
title_full_unstemmed Diabetes in the News: Readability Analysis of Malaysian Diabetes Corpus
title_short Diabetes in the News: Readability Analysis of Malaysian Diabetes Corpus
title_sort diabetes in the news: readability analysis of malaysian diabetes corpus
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9180217/
https://www.ncbi.nlm.nih.gov/pubmed/35682387
http://dx.doi.org/10.3390/ijerph19116802
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