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Mapeando objetivamente as revistas de Farmácia
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Abstract(s)
Introdução: Mapa da ciência é uma representação espacial de como as disciplinas, as áreas do conhecimento, as especialidades, as revistas científicas e, individualmente, os artigos ou os autores estão relacionados entre si. A Farmácia, como área do conhecimento, por englobar características de caráter tecnológico, analítico e assistencial, apresenta ainda uma imprecisão sobre as suas subdivisões em disciplinas e especialidades. Objetivos: Mapear as subdivisões da área da Farmácia a partir dos artigos publicados em revistas científicas. Método: As revistas ativas (dezembro 2016), com publicações em inglês e cujos títulos apresentassem um ou mais termos relacionados à área (pharmacy, pharmaceutica*, pharmacist, pharmacotherapy) foram coletadas das bases Web of Science, Scopus, MEDLINE e PubMed Central. Os títulos de todos os artigos publicados (2006-2016) nas revistas incluídas foram compilados e organizados de acordo com a revista de origem em um corpus único para análise textual (Iramuteq 0.7 alpha 2). Foram conduzidas: análise lexicográfica para determinação dos segmentos de texto (ST) e frequência das palavras; classificação hierárquica descendente (CHD) para categorização das palavras e revistas em grupos lexicais semelhantes; e análise fatorial correspondente (AFC) para obtenção de gráficos bi e trimedimensionais. Qui-quadrado (X2) e p-value foram utilizados como medidas estatísticas de frequência e significância. Resultados: Foram encontradas inicialmente 209 revistas, das quais 161 foram selecionadas. Após a coleta de dados, obtivemos 148.081 títulos de artigos, com média de 919,7±1.069,2 artigos por revista. Na análise lexicográfica, 34.394 ST foram analisados com aproveitamento satisfatório (74,82%). Emergiram 65.070 palavras distintas. Através da CHD foi gerado dendrograma com cinco classes lexicais distintas. As classes foram separadas em duas ramificações principais: subcorpus A (classes 1 e 2); e subcorpus B (classes 3, 4 e 5). As palavras e a revista mais representativas para as classes foram: patient, treatment, therapy, EXP_OP_PHARMACOTHERAPY (p<0,0001) – Classe 1; pharmacy, pharmacist, medication, INT_J_CLIN_PHARM (p<0,0001) – Classe 2; release, formulation, tablet, DRUG_DEVELOP_IND_PHARM (p<0,0001) – Classe 3; activity, extract, synthesis, CHEM_PHARM_BULLETIN (p<0,0001) – Classe 4; cell, expression, human, BIO_PHARM_BULLETIN (p<0,0001) – Classe 5. A AFC gerou dois planos cartesianos (palavras e revistas) que demonstraram clara separação entre o subcorpus A e B, porém, sem clara distinção entre as classes pertencentes ao mesmo subcorpus. Discussão: As análises dos artigos publicados nas revistas do campo de Farmácia nos últimos 10 anos demonstraram uma nítida separação entre duas grandes áreas: Farmácia Clínica (subcorpus A) e Farmácia Básica (subcorpus B). A partir das palavras mais relevantes foi possível identificar as seguintes classes lexicais: 1. Farmacoterapia; 2. Farmácia Prática; 3. Tecnologia Farmacêutica; 4. Farmácia Analítica/Química Farmacêutica; 5. Farmacologia experimental/Biofarmácia. Algumas bases de indexação e vários índices pelos quais se avalia o desempenho de investigadores consideram a Farmácia como uma área única, ignorando as diferenças encontradas nesta análise. Semelhante a outras áreas, é expectável a existência de padrões de publicação e citação diferentes entre as subáreas que justificam a necessidade da subdivisão do campo da Farmácia nas cinco categorias identificadas. Conclusões: Através de um método objetivo de análise textual dos títulos dos artigos científicos foi possível demonstrar a distinção existente no campo da Farmácia entre dois grandes subcorpus e cinco subáreas que a compõem.
Introduction: A science map is a spatial representation of how disciplines, areas of knowledge, specialties, scientific journals, and individually articles or authors are related to each other. Pharmacy, as an area of knowledge, encompasses both technological, analytical and assistance features, which renders imprecise its subdivisions into disciplines and specialties. Main purpose: To map the subdivisions of the Pharmacy area from articles published in scientific journals. Methods: Active journals (December 2016) with publications in English and whose titles exhibited one or more related-terms (pharmacy, pharmaceutica*, pharmacist, pharmacotherapy) were collected from the Web of Science, Scopus, Medline and PubMed Central databases. The titles of all published articles (2006-2016) of the included journals were gathered according to the journal’s source into a single corpus for textual analyses (Iramuteq 0.7 alpha 2). The following analyses were conducted: lexicographic analysis to determine the text segments (ST) and frequency of words; Descending Hierarchical Classification (DHC) to categorize words and journals into similar lexical groups; and Factorial Correspondence Analyses (FCA) to obtain bi and tri-dimensional graphs. Chi-square (X2) and p-value were used as statistical measures of frequency and significance. Results: Initially 209 journals were found, of which 161 were selected. After data collection, 148,081 article’s titles were obtained, with an average of 919.7±1069.2 articles per journal. In the lexicographic analysis, 34,394 ST were analyzed with a satisfactory performance (74.82%). We found 65,070 different words. The DHC generated a dendrogram with five distinct lexical classes. The classes were separated into two main branches: subcorpus A (classes 1 and 2); and subcorpus B (3, 4 and 5). The most representative words and journals for each class were: patient, treatment, therapy, Exp_Op_Pharmacotherapy (p<0.0001) – Class 1; pharmacy, pharmacist, medication, Int_J_Clin_Pharm (p<0.0001) – Class 2; release, formulation, tablet, Drug_Develop_Ind_Pharm (p<0.0001) – Class 3; activity, extract, synthesis, Chem_Pharm_Bulletin (p<0.0001) – Class 4; cell, expression, human, Bio_Pharm_Bulletin (p<0.0001) – Class 5. The two Cartesian planes (words and journals) obtained in the FCA showed a clear separation between subcorpus A and B, but without clear distinction between the classes of the same subcorpus. Discussion: The analyses of the articles published in the Pharmacy journals in the last 10 years highlighted a clear separation between two major areas: Clinical Pharmacy (subcorpus A) and Basic Pharmacy (subcorpus B). The following lexical classes were identified from the most relevant words: 1. Pharmacotherapy; 2. Pharmacy Practice; 3. Pharmaceutical Technology; 4. Analytical Pharmacy/Pharmaceutical Chemistry; 5. Experimental Pharmacology/Biopharmacy. Some indexing bases and several indices by which the performance of researchers are evaluated, consider Pharmacy as a single area, ignoring the differences found in these analyses. Similar to other areas, different publication and citation patterns are expected to exist among subareas, which justify the need to subdivide the Pharmacy field into the five identified categories. Conclusions: Through an objective method of textual analysis of the titles of the scientific articles, it was possible to demonstrate the distinction between two major subcorpus with five sub-areas that compose the Pharmacy field.
Introduction: A science map is a spatial representation of how disciplines, areas of knowledge, specialties, scientific journals, and individually articles or authors are related to each other. Pharmacy, as an area of knowledge, encompasses both technological, analytical and assistance features, which renders imprecise its subdivisions into disciplines and specialties. Main purpose: To map the subdivisions of the Pharmacy area from articles published in scientific journals. Methods: Active journals (December 2016) with publications in English and whose titles exhibited one or more related-terms (pharmacy, pharmaceutica*, pharmacist, pharmacotherapy) were collected from the Web of Science, Scopus, Medline and PubMed Central databases. The titles of all published articles (2006-2016) of the included journals were gathered according to the journal’s source into a single corpus for textual analyses (Iramuteq 0.7 alpha 2). The following analyses were conducted: lexicographic analysis to determine the text segments (ST) and frequency of words; Descending Hierarchical Classification (DHC) to categorize words and journals into similar lexical groups; and Factorial Correspondence Analyses (FCA) to obtain bi and tri-dimensional graphs. Chi-square (X2) and p-value were used as statistical measures of frequency and significance. Results: Initially 209 journals were found, of which 161 were selected. After data collection, 148,081 article’s titles were obtained, with an average of 919.7±1069.2 articles per journal. In the lexicographic analysis, 34,394 ST were analyzed with a satisfactory performance (74.82%). We found 65,070 different words. The DHC generated a dendrogram with five distinct lexical classes. The classes were separated into two main branches: subcorpus A (classes 1 and 2); and subcorpus B (3, 4 and 5). The most representative words and journals for each class were: patient, treatment, therapy, Exp_Op_Pharmacotherapy (p<0.0001) – Class 1; pharmacy, pharmacist, medication, Int_J_Clin_Pharm (p<0.0001) – Class 2; release, formulation, tablet, Drug_Develop_Ind_Pharm (p<0.0001) – Class 3; activity, extract, synthesis, Chem_Pharm_Bulletin (p<0.0001) – Class 4; cell, expression, human, Bio_Pharm_Bulletin (p<0.0001) – Class 5. The two Cartesian planes (words and journals) obtained in the FCA showed a clear separation between subcorpus A and B, but without clear distinction between the classes of the same subcorpus. Discussion: The analyses of the articles published in the Pharmacy journals in the last 10 years highlighted a clear separation between two major areas: Clinical Pharmacy (subcorpus A) and Basic Pharmacy (subcorpus B). The following lexical classes were identified from the most relevant words: 1. Pharmacotherapy; 2. Pharmacy Practice; 3. Pharmaceutical Technology; 4. Analytical Pharmacy/Pharmaceutical Chemistry; 5. Experimental Pharmacology/Biopharmacy. Some indexing bases and several indices by which the performance of researchers are evaluated, consider Pharmacy as a single area, ignoring the differences found in these analyses. Similar to other areas, different publication and citation patterns are expected to exist among subareas, which justify the need to subdivide the Pharmacy field into the five identified categories. Conclusions: Through an objective method of textual analysis of the titles of the scientific articles, it was possible to demonstrate the distinction between two major subcorpus with five sub-areas that compose the Pharmacy field.
Description
Keywords
Mapa da ciência Farmácia