<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0120-4157</journal-id>
<journal-title><![CDATA[Biomédica]]></journal-title>
<abbrev-journal-title><![CDATA[Biomed.]]></abbrev-journal-title>
<issn>0120-4157</issn>
<publisher>
<publisher-name><![CDATA[Instituto Nacional de Salud]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0120-41572020000400702</article-id>
<article-id pub-id-type="doi">10.7705/biomedica.5182</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Estructura de comunidades en las redes semánticas de la investigación biomédica sobre disparidades en salud y sexismo]]></article-title>
<article-title xml:lang="en"><![CDATA[Structure of communities in semantic networks of biomedical research on disparities in health and sexism]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rivera-Romano]]></surname>
<given-names><![CDATA[Lucero Soledad]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Juárez-Cano]]></surname>
<given-names><![CDATA[Gabriela]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hernández-Lemus]]></surname>
<given-names><![CDATA[Enrique]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Vallejo]]></surname>
<given-names><![CDATA[Maite]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Martínez-García]]></surname>
<given-names><![CDATA[Mireya]]></given-names>
</name>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma de la Ciudad de México  ]]></institution>
<addr-line><![CDATA[México ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto Nacional de Medicina Genómica  ]]></institution>
<addr-line><![CDATA[México ]]></addr-line>
<country>México</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Instituto Nacional de Cardiología Ignacio Chávez  ]]></institution>
<addr-line><![CDATA[México ]]></addr-line>
<country>México</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2020</year>
</pub-date>
<volume>40</volume>
<numero>4</numero>
<fpage>702</fpage>
<lpage>721</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0120-41572020000400702&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0120-41572020000400702&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0120-41572020000400702&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Introducción. Como una iniciativa para mejorar la calidad de la atención sanitaria, en la investigación biomédica se ha incrementado la tendencia centrada en el estudio de las disparidades en salud y sexismo.  Objetivo. Caracterizar la evidencia científica sobre la disparidad en salud definida como la brecha existente entre la distribución de la salud y el posible sesgo por sexo en el acceso a los servicios médicos.  Materiales y métodos. Se hizo una búsqueda simultánea de la literatura científica en la base de datos Medline PubMed de dos descriptores fundamentales: Healthcare disparities y Sexism. Posteriormente, se construyó una red semántica principal y se determinaron algunas subunidades estructurales (comunidades) para el análisis de los patrones de organización de la información. Se utilizó el programa de código abierto Cytoscape para el analisis y la visualización de las redes y el MapEquation, para la detección de comunidades. Asimismo, se desarrolló código ex profeso disponible en un repositorio de acceso público.  Resultados. El corpus de la red principal mostró que los términos sobre las enfermedades del corazón fueron los descriptores de condiciones médicas más concurrentes. A partir de las subunidades estructurales, se determinaron los patrones de información relacionada con las políticas públicas, los servicios de salud, los factores sociales determinantes y los factores de riesgo, pero con cierta tendencia a mantenerse indirectamente conectados con los nodos relacionados con condiciones médicas.  Conclusiones. La evidencia científica indica que la disparidad por sexo sí importa para la calidad de la atención de muchas enfermedades, especialmente aquellas relacionadas con el sistema circulatorio. Sin embargo, aún se percibe un distanciamiento entre los factores médicos y los sociales que dan lugar a las posibles disparidades por sexo.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Introduction: As an initiative to improve the quality of health care, the trend in biomedical research focused on health disparities and sex has increased.  Objective: To carry out a characterization of the scientific evidence on health disparity defined as the gap between the distribution of health and the possible gender bias for access to medical services.  Materials and methods: We conducted a simultaneous search of two fundamental descriptors in the scientific literature in the Medline PubMed database: healthcare disparities and sexism. Subsequently, a main semantic network was built and some structural subunits (communities) were identified for the analysis of information organization patterns. We used open-source software: Cytoscape to analyze and visualize the semantic network, and MapEquation for community detection, as well as an ad hoc code available in a public access repository.  Results: The core network corpus showed that the terms on heart disease were the most common among the descriptors of medical conditions. Patterns of information related to public policies, health services, social determinants, and risk factors were identified from the structural subunits, but with a certain tendency to remain indirectly connected to the nodes of medical conditions.  Conclusions: Scientific evidence indicates that gender disparity does matter for the care quality in many diseases, especially those related to the circulatory system. However, there is still a gap between the medical and social factors that give rise to possible disparities by sex.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[investigación biomédica]]></kwd>
<kwd lng="es"><![CDATA[calidad de la atención de salud]]></kwd>
<kwd lng="es"><![CDATA[disparidades en el estado de salud]]></kwd>
<kwd lng="es"><![CDATA[sexismo]]></kwd>
<kwd lng="es"><![CDATA[minería de datos]]></kwd>
<kwd lng="es"><![CDATA[interpretación estadística de datos]]></kwd>
<kwd lng="es"><![CDATA[web semántica]]></kwd>
<kwd lng="en"><![CDATA[Biomedical research]]></kwd>
<kwd lng="en"><![CDATA[quality of health care]]></kwd>
<kwd lng="en"><![CDATA[health status disparities]]></kwd>
<kwd lng="en"><![CDATA[sexism]]></kwd>
<kwd lng="en"><![CDATA[data mining]]></kwd>
<kwd lng="en"><![CDATA[data interpretation]]></kwd>
<kwd lng="en"><![CDATA[statistical]]></kwd>
<kwd lng="en"><![CDATA[semantic web]]></kwd>
</kwd-group>
</article-meta>
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