RESUMO
Aim: Integrating sex and gender into health research is critical to contributing to an ethical and more responsible science to address significant knowledge gaps, resulting in higher-quality evidence for all. Subject and methods: Using the Essential Metrics for Assessing Sex and Gender Integration in Health Research Proposals Involving Human Participants, we evaluate the quality of the integration of sex and gender in the 350 scientific articles produced by 144 health studies funded by the Department of Science and Technology of the Brazilian Ministry of Health between 2004 and 2016. Results: The results show that clinical research articles are the type of studies that most frequently report on sex differences, while population and public health research articles most frequently report on gender differences. Analysis of the quality of sex and gender integration reveals low levels of qualification in the items of the literature review and research objectives (section 1) and participant recruitment and retention (section 2). However, the data collection tools, data analysis, and knowledge translation (section 3) items were rated as excellent and good. Conclusion: Funding agencies and public institutions should recognize the importance of the integration of sex and gender at all stages of the research process, for instance, through awareness and training for researchers and reviewers, clear requirements, and the possibility to use metrics in the evaluations process.
RESUMO
INTRODUCTION: Scientometrics analyzes scientific publications through bibliometric and computational techniques, whereby productivity and impact indicators are generated. OBJECTIVE: To propose a multidimensional methodology in order to obtain the scientometric profile of the National Cancer Institute (INCan), Mexico, and rank it with regard to other national health institutions. METHOD: Using the LabSOM software and the ViBlioSOM methodology based on artificial neural networks, the INCan scientific production indexed in the Web of Science from 2007 to 2017 was analyzed. The multidimensional scientometric profile of the Institute was obtained and compared with that of other national health institutions. RESULTS: In terms of productivity, INCan ranks fourth among the 10 Mexican public health institutions indexed in the Web of Science; in the normalized impact ranking, it ranks sixth. Although out of 1323 articles 683 (51.62 %) did not receive citations, 11 articles classified as excellent (0.83 %) obtained 24 % of 11,932 citations and, consequently, INCan normalized impact rate showed a mean productivity higher than the world mean. CONCLUSION: Multidimensional analysis with the proposed neural network enables obtaining a more reliable and comprehensive absolute and relative institutional scientiometric profile than that derived from measuring isolated variables.
INTRODUCCIÓN: La cienciometría permite analizar la productividad e impacto de las publicaciones científicas mediante técnicas bibliométricas y computacionales. OBJETIVO: Proponer una metodología multidimensional para obtener el perfil cienciométrico del Instituto Nacional de Cancerología (INCan), México, y compararlo respecto a otras instituciones nacionales de salud. MÉTODO: Con el programa LabSOM y la metodología ViBlioSOM, basada en redes neuronales artificiales, se analizó la producción científica del INCan indexada en la Web of Science entre 2007 y 2017. Se obtuvo el perfil cienciométrico multidimensional del Instituto y se comparó con el de otras instituciones nacionales de salud. RESULTADOS: En productividad, el INCan ocupa el cuarto lugar de las 10 instituciones mexicanas de salud pública indexadas en la Web of Science.; en el ranking de impacto normalizado, el sexto lugar. Aun cuando de 1323 artículos, 683 (51.62 %) no recibieron citas, 11 artículos de excelencia (0.83 %) lograron 24 % de 11 932 citas y, consecuentemente, el impacto normalizado del INCan evidenció una productividad media por arriba de la media mundial. CONCLUSIÓN: El análisis multidimensional con la red neuronal propuesta permite obtener un perfil cienciométrico institucional absoluto y relativo más fidedigno e integral que el derivado de conteos de variables aisladas.
Assuntos
Academias e Institutos/estatística & dados numéricos , Bibliometria , Pesquisa Biomédica/estatística & dados numéricos , Oncologia/estatística & dados numéricos , Indexação e Redação de Resumos/estatística & dados numéricos , Academias e Institutos/classificação , Eficiência Organizacional/estatística & dados numéricos , México , Redes Neurais de ComputaçãoRESUMO
Resumen Introducción: La cienciometría permite analizar la productividad e impacto de las publicaciones científicas mediante técnicas bibliométricas y computacionales. Objetivo: Proponer una metodología multidimensional para obtener el perfil cienciométrico del Instituto Nacional de Cancerología (INCan), México, y compararlo respecto a otras instituciones nacionales de salud. Método: Con el programa LabSOM y la metodología ViBlioSOM, basada en redes neuronales artificiales, se analizó la producción científica del INCan indexada en la Web of Science entre 2007 y 2017. Se obtuvo el perfil cienciométrico multidimensional del Instituto y se comparó con el de otras instituciones nacionales de salud. Resultados: En productividad, el INCan ocupa el cuarto lugar de las 10 instituciones mexicanas de salud pública indexadas en la Web of Science.; en el ranking de impacto normalizado, el sexto lugar. Aun cuando de 1323 artículos, 683 (51.62 %) no recibieron citas, 11 artículos de excelencia (0.83 %) lograron 24 % de 11 932 citas y, consecuentemente, el impacto normalizado del INCan evidenció una productividad media por arriba de la media mundial. Conclusión: El análisis multidimensional con la red neuronal propuesta permite obtener un perfil cienciométrico institucional absoluto y relativo más fidedigno e integral que el derivado de conteos de variables aisladas.
Abstract Introduction: Scientometrics analyzes scientific publications through bibliometric and computational techniques, whereby productivity and impact indicators are generated. Objective: To propose a multidimensional methodology in order to obtain the scientometric profile of the National Cancer Institute (INCan), Mexico, and rank it with regard to other national health institutions. Method: Using the LabSOM software and the ViBlioSOM methodology based on artificial neural networks, the INCan scientific production indexed in the Web of Science from 2007 to 2017 was analyzed. The multidimensional scientometric profile of the Institute was obtained and compared with that of other national health institutions. Results: In terms of productivity, INCan ranks fourth among the 10 Mexican public health institutions indexed in the Web of Science; in the normalized impact ranking, it ranks sixth. Although out of 1323 articles 683 (51.62 %) did not receive citations, 11 articles classified as excellent (0.83 %) obtained 24 % of 11,932 citations and, consequently, INCan normalized impact rate showed a mean productivity higher than the world mean. Conclusion: Multidimensional analysis with the proposed neural network enables obtaining a more reliable and comprehensive absolute and relative institutional scientiometric profile than that derived from measuring isolated variables.