Connections and Biases in Health Equity and Culture Research: A Semantic Network Analysis

Health equity is a rather complex issue. Social context and economical disparities, are known to be determining factors. Cultural and educational constrains however, are also important contributors to the establishment and development of health inequities. As an important starting point for a compre...

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Autores principales: Martínez-García, Mireya, Villegas Camacho, José Manuel, Hernández-Lemus, Enrique
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9002348/
https://www.ncbi.nlm.nih.gov/pubmed/35425756
http://dx.doi.org/10.3389/fpubh.2022.834172
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author Martínez-García, Mireya
Villegas Camacho, José Manuel
Hernández-Lemus, Enrique
author_facet Martínez-García, Mireya
Villegas Camacho, José Manuel
Hernández-Lemus, Enrique
author_sort Martínez-García, Mireya
collection PubMed
description Health equity is a rather complex issue. Social context and economical disparities, are known to be determining factors. Cultural and educational constrains however, are also important contributors to the establishment and development of health inequities. As an important starting point for a comprehensive discussion, a detailed analysis of the literature corpus is thus desirable: we need to recognize what has been done, under what circumstances, even what possible sources of bias exist in our current discussion on this relevant issue. By finding these trends and biases we will be better equipped to modulate them and find avenues that may lead us to a more integrated view of health inequity, potentially enhancing our capabilities to intervene to ameliorate it. In this study, we characterized at a large scale, the social and cultural determinants most frequently reported in current global research of health inequity and the interrelationships among them in different populations under diverse contexts. We used a data/literature mining approach to the current literature followed by a semantic network analysis of the interrelationships discovered. The analyzed structured corpus consisted in circa 950 articles categorized by means of the Medical Subheadings (MeSH) content-descriptor from 2014 to 2021. Further analyses involved systematic searches in the LILACS and DOAJ databases, as additional sources. The use of data analytics techniques allowed us to find a number of non-trivial connections, pointed out to existing biases and under-represented issues and let us discuss what are the most relevant concepts that are (and are not) being discussed in the context of Health Equity and Culture.
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spelling pubmed-90023482022-04-13 Connections and Biases in Health Equity and Culture Research: A Semantic Network Analysis Martínez-García, Mireya Villegas Camacho, José Manuel Hernández-Lemus, Enrique Front Public Health Public Health Health equity is a rather complex issue. Social context and economical disparities, are known to be determining factors. Cultural and educational constrains however, are also important contributors to the establishment and development of health inequities. As an important starting point for a comprehensive discussion, a detailed analysis of the literature corpus is thus desirable: we need to recognize what has been done, under what circumstances, even what possible sources of bias exist in our current discussion on this relevant issue. By finding these trends and biases we will be better equipped to modulate them and find avenues that may lead us to a more integrated view of health inequity, potentially enhancing our capabilities to intervene to ameliorate it. In this study, we characterized at a large scale, the social and cultural determinants most frequently reported in current global research of health inequity and the interrelationships among them in different populations under diverse contexts. We used a data/literature mining approach to the current literature followed by a semantic network analysis of the interrelationships discovered. The analyzed structured corpus consisted in circa 950 articles categorized by means of the Medical Subheadings (MeSH) content-descriptor from 2014 to 2021. Further analyses involved systematic searches in the LILACS and DOAJ databases, as additional sources. The use of data analytics techniques allowed us to find a number of non-trivial connections, pointed out to existing biases and under-represented issues and let us discuss what are the most relevant concepts that are (and are not) being discussed in the context of Health Equity and Culture. Frontiers Media S.A. 2022-03-29 /pmc/articles/PMC9002348/ /pubmed/35425756 http://dx.doi.org/10.3389/fpubh.2022.834172 Text en Copyright © 2022 Martínez-García, Villegas Camacho and Hernández-Lemus. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Martínez-García, Mireya
Villegas Camacho, José Manuel
Hernández-Lemus, Enrique
Connections and Biases in Health Equity and Culture Research: A Semantic Network Analysis
title Connections and Biases in Health Equity and Culture Research: A Semantic Network Analysis
title_full Connections and Biases in Health Equity and Culture Research: A Semantic Network Analysis
title_fullStr Connections and Biases in Health Equity and Culture Research: A Semantic Network Analysis
title_full_unstemmed Connections and Biases in Health Equity and Culture Research: A Semantic Network Analysis
title_short Connections and Biases in Health Equity and Culture Research: A Semantic Network Analysis
title_sort connections and biases in health equity and culture research: a semantic network analysis
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9002348/
https://www.ncbi.nlm.nih.gov/pubmed/35425756
http://dx.doi.org/10.3389/fpubh.2022.834172
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