Trends and Growth in ADHD Research: A Bibliometric Analysis of Indian Literature Indexed in Scopus

Trends and Growth in ADHD Research: A Bibliometric Analysis of Indian Literature Indexed in Scopus | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Trends and Growth in ADHD Research: A Bibliometric Analysis of Indian Literature Indexed in Scopus Aswin Kollamkandipalliyali, Sisira Kizhalath, Gangadharan Ragesh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5888968/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: Attention-Deficit/Hyperactivity Disorder (ADHD) is a widely studied neurodevelopmental disorder with increasing research contributions from India. This study examines the impact of Indian ADHD research, focusing on publication growth, research trends, and resource impact. Using bibliometric methods, it identifies key institutions, authors, funding agencies, and current research trends shaping the field. Methods: Data for this study were sourced from the Scopus bibliographic database, retrieving 721 documents affiliated with India. Bibliometric analysis was conducted using the bibliometrix R package. Bradford's Law was applied to identify core journals, Lotka's Law was used to assess author productivity, and keyword analysis was used to determine prevalent research themes. Results: India is a leading contributor to ADHD research, with the highest publication count in 2023. Approximately 36.1% of research publications on ADHD are within medicine. The National Institute of Mental Health and Neurosciences (NIMHANS) emerged as the most productive institution, while Prof.Sinha was identified as the most prolific author. The Indian Council of Medical Research (ICMR) was the top funding agency. Bradford’s Law revealed 21 core journals publishing ADHD research, and keyword analysis highlighted contemporary research themes. Conclusion: This study highlights India's significant role in ADHD research, driven by contributions from key institutions, prolific authors, and robust funding. Identifying core journals and emerging research trends offers valuable insights for future research directions in this field. Library Science Psychiatry Attention-Deficit Hyperactivity Disorder Bibliometric Publication Growth Research Trends Bibliometrix R Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION Attention-deficit/hyperactivity disorder is the most well-known neurodevelopmental disorder ( 1 ). Approximately 5–7% of student’s worldwide experience ADHD, which is distinguished by the apparent symptoms of inattention, hyperactivity, and impulsivity( 1 , 2 ). ADHD is one of the most extensively researched disorders in mental health, and leading indexing databases like Scopus, Web of Science, and PubMed host vast collections of ADHD and associated research. This present research utilized bibliometric analysis to study the ADHD publications from India indexed in Scopus from 2014 to 2023. Bibliometrics, a globally adopted method, examines the impact of research output. Such analysis aids in identifying highly productive countries, organizations, resources, and authors within specific research fields. Concepts like Bradford's Law of Scattering( 3 ) and Lotka's Law( 4 ) help pinpoint core journals and author productivity. The increasing impact of scholarly resources reflects researchers' interest and the influence of research articles in a field ( 5 ). Numerous mental health researchers use bibliometric methods and concepts to identify the progression and trends within various mental health domains. These encompass schizophrenia and inflammation ( 6 ), Neuroimaging in Psychiatric Disorders( 7 ), Psychopathology and mental health ( 8 ), Perinatal anxiety( 9 ), etc. Several bibliometric studies have focused on ADHD. For instance, a group of researchers from medical institutes in China conducted a bibliometric study of 1975 articles on gut microbiota in ADHD ( 10 ). The Web of Science was chosen for data retravel. The researchers identified the United States, China, and Spain as the leading countries with the most published articles. Additionally, the research looked at journal distribution, authorship pattern, co-citation analysis, and keyword analysis. The scientometric approach identified the major themes and trends in ADHD research during the last decade. 284381 publications collected from the Web of Science were analyzed( 1 ). The study results have identified four major research areas: 1) ADHD treatment, risk factors, and evidence synthesis; 2) neurophysiology, neuropsychology, and neuroimaging; 3) genetics; 4) comorbidities. A research paper published in the Annals of General Psychiatry analyzed the 100 highly cited articles on ADHD since 2014( 5 ). The researchers retrieved bibliographic information for the articles from PubMed. Their findings revealed that epidemiology emerged as the most popular field of study, with the United States providing the most significant contributions. Among the 100 highly cited publications, the Journal of the American Academy of Child and Adolescent Psychiatry had the highest number of research articles (15%). In additional studies, Denche-Zamorano and co-researcher used bibliometric approaches in psychometry to identify current research trends ( 11 ). Similarly, Haiyin Deng and colleagues conducted a bibliometric analysis of ADHD to identify hotspots and developmental trends in neuroimaging during the last three decades ( 12 ). These are the core objectives of the study: To evaluate the development of research in ADHD. To identify the most frequently selected source journals and their impact. To pinpoint the most influential institute affiliations and authors. To analyze keywords to identify recent research trends. METHODS This Bibliometric analysis was performed using Scopus data. Scopus indexed 324131 documents on ADHD-related topics between 2014 and 2023. The publications from 2014 to 2023 from India associated with ADHD were selected for this analysis. A total of 721 indexed from India were searched and retrieved from March 22, 2024, on the advanced query terms TITLE-ABS-KEY (Attention-deficit/hyperactivity disorder OR ADHD). Data analysis involves two steps. Firstly, the Scopus interface was utilized to analyze publication summaries and documents categorized by subjects and the most productive institution. Secondly, the bibliometrix R package will apply Bradford's Law to identify core journals and Lotka's Law to ascertain the most productive authors. The dataset was downloaded in .bib format for this analysis, and open-source software VOSviewer was utilized to visualize keyword analysis. Finally, the findings were organized into distinct categories RESULT AND DISCUSSION 1. Publication summary A thorough analysis of 718 papers from Scopus on ADHD encompassed 439 (61.1%) articles, 120 (16.7%) review articles, 61(9.6%) conference proceedings, 39 (5.4%) book chapters, 33(4.6%) letters to the editor, 8 (1.1%) editorials, and 10(1.5%) other types of contributions. All of the publications were published only in English. The annual growth of ADHD publications from India is shown in Fig. 1 . There were 31 documents from India in the initial year of 2014; this number significantly increased to 40 in 2015, 43 in 2016, and 45 in 2017. In 2018, there was a decrease in the growth trend, with 40 documents recorded. The number of documents increased significantly over the next five years, from 63 articles in 2019 to 163 in 2023. A substantial rise was observed, with 83 documents in 2021 and 163 in 2023. India stands out as one of the foremost contributors to ADHD research in Asia, securing fourth place and ranking 20th globally. Articles on ADHD from India showed a growing trend over the past decade, particularly since 2018. Most articles on ADHD published in India fall under the subjects of Medicine (36.1%), Psychology (10%), and Neuroscience (9%). ADHD research is critically important in India due to the country's diverse population, cultural nuances, and unique socio-economic challenges. Early and effective management of ADHD can significantly impact children’s academic and social development. Research can help develop culturally appropriate diagnostic tools, interventions, and policies tailored to India’s needs. It also highlights the potential benefits of alternative therapies, making treatment more accessible and acceptable to the population ( 13 ). 2. Document by subjects Scopus indexed a total of 718 documents about ADHD across various disciplines (see Fig. 2 ). With the biggest share of 36.1%, medicine was the most popular field, followed by psychology (10%), neuroscience (9%), computer science (8.3%), and biochemistry (7.5%). The analysis indicates a significant use of artificial intelligence and computer science in mental health research, particularly in Indian studies on ADHD. This claim is supported by the document shares from engineering (5.8%) and computer science (8.3%). With an emphasis on ADHD research, these technologies have developed into vital tools for advancing mental health research. The seamless transition between computer science and artificial intelligence allows researchers to investigate creative methods and fixes, improving knowledge and tackling mental health issues ( 5 , 14 ). 3. Most productive institutions Around 165 institutes are actively engaged in contributing to ADHD research. Figure 3 illustrates the 15 most productive institutes. Notably, researchers and faculty associated with the National Institute of Mental Health and Neurosciences (NIMHANS) in Bangalore have emerged as the leading contributors, publishing 61 documents. As a premier mental health research institution and an “Institute of National Importance” recognized by the Government of India, NIMHANS's significant role in ADHD studies is likely due to its robust infrastructure and expertise in mental health. Following closely is the All India Institute of Medical Sciences (AIIMS) in New Delhi with 39 publications and the Postgraduate Institute of Medical Education and Research (PGIMER) in Chandigarh with 29 documents. Three institutes stand out in terms of current research on ADHD: NIMHANS, AIIMS, and PGIMER. Between 2020 and 2023, NIMHANS released 29 papers on ADHD; in the same time frame, AIIMS Delhi produced 18 new papers. PGIMER has contributed fifteen new papers to the corpus of research on ADHD. Identifying such active institutions and productive authors is essential for fostering collaborations, advancing interdisciplinary research, and strengthening research networks. Beyond these Indian institutions, foreign institutes have also contributed to recent ADHD research, indicating international collaboration. Noteworthy mentions include Harvard Medical School in the USA, contributing 15 documents; Karolinska Institute in Sweden, with 14 documents; Universidade Federal do Rio Grande do Sul in Brazil, publishing 13 documents; and the University of Cape Town, contributing 13. These international contributions highlight the collaborative efforts between Indian institutes and affiliated authors on the global stage ( 15 ). 4. Core Journal The articles on ADHD in India were disseminated across 165 different sources. Table 1 presents the top 15 highly productive sources, showcasing the number of documents, impact factor, and journal quartile. The impact factor, determined by Clarivate Analytics, serves as a crucial metric for assessing journal quality ( 16 ). It measures the frequency of citations received by selected articles in recent years. Additionally, the Scopus Quartiles offer a metric to evaluate a journal's influence( 17 ). These quartiles assess a journal's citation performance relative to all other journals in the Scopus. They are classified into four categories: Q1 (green) represents the top quarter of journals with the highest values, Q2 (yellow) includes the second-highest values, Q3 (orange) encompasses the third-highest values, and Q4 (red) denotes the lowest values. The “Journal of the Indian Association for Child and Adolescent Mental Health” stands out with the highest number of documents (25) but holds the lowest impact factor of 0.6, placing it in Q4; this may be articles not addressing critical or globally relevant ADHD research topics, limited readership, indexing as a regional journal, or focusing on recent publications that haven't had time to gain citations. On the other hand, the “Asian Journal of Psychiatry” has 22 documents, boasts the highest impact factor (9.5), and is classified in Q1. Most journals (n = 11) are positioned in Q1 or Q2, indicating a significant influence within their core fields. Notably, the “Journal of Postgraduate Medicine” “The Journal of the Indian Association for Child and Adolescent Mental Health” “Advances in Intelligent Systems and Computing” and the “Indian Journal of Practical Pediatrics” exhibit the lowest values in this analysis. To gain a comprehensive understanding of the current state of research in ADHD and explore its potential future directions, identifying the most productive journals that consistently publish high-quality articles is crucial. Such journals are pivotal in disseminating groundbreaking research, advancing knowledge, and shaping clinical practices. Table 1 most productive journals Source Title Documents TC Impact Factor Quartiles Journal Of Indian Association for Child and Adolescent Mental Health 25 26 0.6 Q4 Asian Journal of Psychiatry 22 178 9.5 Q1 Indian Journal of Psychiatry 22 125 3.1 Q2 Indian Journal of Psychological Medicine 19 141 2.8 Q2 Indian Journal of Pediatrics 14 33 4.3 Q2 Indian Pediatrics 12 125 2.3 Q2 Journal of Clinical Psychiatry 11 62 5.3 Q1 Journal of Attention Disorders 10 161 3.0 Q1 Journal of Postgraduate Medicine 8 227 - Q3 Indian Journal of Medical Research 7 30 4.2 Q2 Research Journal of Pharmacy and Technology 7 11 - Q2 Advances In Intelligent Systems and Computing 6 5 - Q4 European Child and Adolescent Psychiatry 6 170 6.4 Q1 Indian Journal of Practical Pediatrics 6 3 - Q4 Scientific Reports 6 22 4.6 Q1 *TC = Total citation Coined by Samuel Bradford in 1934, the Bradford Law of Scattering has been instrumental in identifying core journals within specific subject domains. Figure 4 illustrates the core journals identified through Bradford's Law, with Zone 1 encompassing 21 journals. Notably, Table 1 supports this, as the listed 15 most productive journals align with the core journals identified by Bradford's Law. 5. Most Influential Authors Regarding author-level analysis, the findings initially highlight the most productive authors, considering their impact on resources. Subsequently, they examine the authors' production trends over time and conclude by exploring the countries of the corresponding authors. During the study period, 3393 authors contributed to publications related to ADHD; Table 2 presents the top ten authors with their resource impact. Sinha S emerged as the most productive author, publishing 22 documents on ADHD and receiving 213 citations, resulting in an h-index of 9. Following closely is Mukhopadhyay K, with 21 publications on ADHD, garnering 168 citations and achieving an h-index of 8. Sagar S secured the third position with 19 publications, accumulating 557 citations and achieving an h-index of 8. Table 2 The most productive author with their resource impact Authors Publications TC H Index G Iindex M Index SINHA S 22 213 9 14 0.818 MUKHOPADHYAY K 21 168 8 12 0.727 SAGAR R 19 557 8 19 0.727 KARANDE S 17 351 10 17 0.909 SINGH A 15 478 6 15 0.545 ANDRADE C 12 456 8 12 0.889 BENEGAL V 12 76 6 8 0.545 MAITRA S 12 66 5 7 0.556 CHATTERJEE M 11 223 8 11 0.8 JAISOORYA TS 11 166 7 11 0.7 THENNARASU K 11 48 4 6 0.5 ARUN P 10 292 9 10 0.818 DE VRIES PJ 10 79 5 8 0.455 GUPTA A 10 35 4 5 0.364 SHARMA A 10 167 4 10 0.364 *TC = Total Citations Lotka's Law, coined by Alfred J. Lotka ( 4 ), describes the correlation between the number of authors and the number of articles within a specific subject domain. It suggests that a small group of highly productive authors will contribute a significant fraction of the published papers. In a sample illustrated in Fig. 6 , the application of Lotka's Law reveals that 84.4% of authors contributed only one article each, while 8.7% published two papers on average. Additionally, approximately 3.3% of authors authored three papers, 1.5% contributed four publications each, and 0.5% published five papers. A smaller percentage of authors was also observed, ranging from those who contributed six to ten articles—notably, only five authors published more than 15 articles each. Lotka's Law asserts that a small fraction of remarkably productive writers will generate the bulk of publications within a given field. Conversely, most authors will contribute only a negligible number of papers individually. 6. Highly cited article Citation analysis plays a significant role in bibliometric analysis by assessing the productivity of articles( 18 ). A high number of citations indicates an article's global and local influence within a research field. Table 3 presents the top 10 highly cited articles, their source journals, and citation counts. These articles have an average of 150 citations each, with the first two being review articles and the third a meta-analysis. Notably, two systematic reviews and meta-analyses are included in this list. It's worth mentioning that these highly cited articles are published in open-access journals, facilitating global recognition and free access to information. Table 3 highly cited articles. Title Source Title Cited by OA The World Federation of ADHD International Consensus Statement: 208 Evidence-based conclusions about the disorder Neuroscience and Biobehavioral Reviews 421 Yes Brain charts for the human lifespan Nature 348 Yes Psychological and Behavioral Impact of Lockdown and Quarantine Measures for COVID-19 Pandemic on Children, Adolescents and Caregivers: A Systematic Review and Meta-Analysis Journal of Tropical Pediatrics 343 Yes The burden of mental disorders across the states of India: the Global Burden of Disease Study 1990–2017 The Lancet Psychiatry 313 Yes Methylphenidate for children and adolescents with attention deficit hyperactivity disorder (ADHD) Cochrane Database of Systematic Reviews 239 Yes The global coverage of prevalence data for mental disorders in children and adolescents Epidemiology and Psychiatric Sciences 224 Yes Psychiatric disorders and obesity: A review of association studies Journal of Postgraduate Medicine 206 Yes Prevalence of comorbid psychiatric disorders among people with autism spectrum disorder: An umbrella review of systematic reviews and meta-analyses Psychiatry Research 176 Yes Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of harmful effects in non-randomised studies Cochrane Database of Systematic Reviews 175 Yes Physiological and Functional Basis of Dopamine Receptors and Their Role in Neurogenesis: Possible Implication for Parkinson's Disease Journal of Experimental Neuroscience 155 Yes 7. Keyword analysis of ADHD research Figure 7 illustrates the keyword analysis of ADHD-related documents published in India. In Fig. 7 A, a network visualization depicts these keywords. Each node reflects a specific keyword, with node size indicating the frequency of occurrence. The distance separating two nodes signifies the intensity of their correlation. The keywords with closer distance were classified into the same cluster and reflected the core topics. Keywords with closer distances were grouped into the same cluster, representing core topics. Cluster 1 is highlighted in green, emphasizing key ADHD-related terms such as Human, Adolescent, India, Psychometry, Learning disabilities, and others. In Fig. 7 B, an overlay visualization of keywords is depicted, with earlier appearances of keywords marked in blue and recent additions highlighted in yellow. Initially, prevalent topics included "Atomoxetine," "DSM IV," "Psychology," "India," "Genetic association," and "Distress syndrome." Conversely, recent years have witnessed increased interest in keywords such as "Microglia," "autism spectrum disorder (ASD)," "Inflammation," "Herbal medicine," "Machine learning," "Cocaine," "Benzodiazepine," and "Celiac disease." Figure 7 C displays the top frequent keywords in ADHD research. "Human" emerges as the most frequently used author keyword, with 485 occurrences, followed by "male" with 417, "attention deficit hyperactivity disorders" with 373, and "adolescents" with 223. 8. Major funding organization Funding plays a crucial role in supporting scientific research. Upon analyzing the financial sources of the 718 retrieved documents, it was found that 354 documents did not acknowledge any funding sources. However, funding support for the remaining 364 documents was provided by 159 different funding agencies from various countries. Table 3 presents the top 15 funding agencies, their respective countries, and the number of funded articles. Funding agencies from India provided financial support for 104 research studies. The Indian Council of Medical Research (ICMR) emerged as the most prolific funding agency, supporting 14 research studies over 10 years. Following closely behind, the University Grants Commission (UGC) sponsored 9 research studies. At the same time, the Council of Scientific and Industrial Research (CSIR) and the Department of Science and Technology, Ministry of Science and Technology, India, each supported 6 research studies. Additionally, the Department of Biotechnology, Ministry of Science and Technology, India, contributed to 5 research studies, rounding up the top 5 most productive funding agencies from India. Furthermore, agencies from the USA, Sweden, Brazil, Australia, Switzerland, and Belgium were featured in this list. Specifically, the USA supported 25 research studies, Sweden supported 21, Australia supported 19, Brazil supported 14, and Switzerland supported 9 research studies. Table 4 Top 15 funding organizations for ADHD research Funding organization Country No funded article Indian Council of Medical Research India 14 National Institutes of Health USA 12 National Institute of Mental Health USA 10 University Grants Commission India 9 Vetenskapsrådet- Swedish Research Council Sweden 8 Conselho Nacional de Desenvolvimento Científico e Tecnológico (National Council for Scientific and Technological Development) Brazil 6 Council of Scientific and Industrial Research, India India 6 Department of Science and Technology, Ministry of Science and Technology, India India 6 Eli Lilly and Company USA 6 National Health and Medical Research Council Australia 6 Novartis Switzerland 6 Svenska Forskningsrådet Formas Sweden 6 VINNOVA- Swedish Governmental Agency for Innovation Systems Sweden 6 Department of Biotechnology, Ministry of Science and Technology, India India 5 European Commission Belgium 5 CONCLUSION In conclusion, based on Scopus data, this bibliometric analysis offers a comprehensive overview of the Indian ADHD research landscape, highlighting publication trends, leading authors, productive journals, funding agencies, and key research areas. The structured findings serve as a valuable resource for the mental health research community and professionals, enabling them to identify emerging trends in ADHD research. Additionally, these insights can assist policymakers in shaping effective research funding strategies and supporting innovative studies while showcasing India’s contributions to ADHD research. For librarians and library professionals, this analysis aids in identifying high-impact journals and enhancing resource curation and accessibility. References Cortese S, Sabé M, Chen C, Perroud N, Solmi M (2022) Half a century of research on Attention-Deficit/Hyperactivity Disorder: A scientometric study. 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subjects\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5888968/v1/ea8b528cdb0efd63a5487a1d.jpg"},{"id":74687891,"identity":"00de87b8-7c96-4a10-9311-4b62e4a087c9","added_by":"auto","created_at":"2025-01-24 17:33:07","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":86979,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3 Most productive institutions\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5888968/v1/18d8798a4e9103702eac11bc.jpg"},{"id":74687893,"identity":"07311337-8e68-4cf3-9251-597b42a25aa9","added_by":"auto","created_at":"2025-01-24 17:33:07","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":108903,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 4 Applying Bradford's Law to Find Core Journals\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5888968/v1/9a29301da7fd15772bb52468.jpg"},{"id":74688373,"identity":"15a52e8a-8902-4857-bdf5-f1cd70f31197","added_by":"auto","created_at":"2025-01-24 17:41:07","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":115502,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 6 Authors' productivity through Lotka's law\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5888968/v1/bf2379694d1c1edfc6d300d7.jpg"},{"id":74688376,"identity":"d5b35159-f1d9-4e47-b29f-e7c57e63a057","added_by":"auto","created_at":"2025-01-24 17:41:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":483335,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 7 Keyword analysis\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5888968/v1/b0efb5e964c2b03c562b9b8a.png"},{"id":74689386,"identity":"03d16e98-58bd-473e-a19d-3baa907b7a84","added_by":"auto","created_at":"2025-01-24 17:57:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1674209,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5888968/v1/d756025a-aa15-4066-9ba3-27aefd43c1dd.pdf"},{"id":74687890,"identity":"f1503d64-f9bd-4964-a3b7-a1179e356c45","added_by":"auto","created_at":"2025-01-24 17:33:07","extension":"enl","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":45056,"visible":true,"origin":"","legend":"","description":"","filename":"ADHD.enl","url":"https://assets-eu.researchsquare.com/files/rs-5888968/v1/f2279dfe99ce11c015de9e8a.enl"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eTrends and Growth in ADHD Research: A Bibliometric Analysis of Indian Literature Indexed in Scopus\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAttention-deficit/hyperactivity disorder is the most well-known neurodevelopmental disorder (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Approximately 5\u0026ndash;7% of student\u0026rsquo;s worldwide experience ADHD, which is distinguished by the apparent symptoms of inattention, hyperactivity, and impulsivity(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). ADHD is one of the most extensively researched disorders in mental health, and leading indexing databases like Scopus, Web of Science, and PubMed host vast collections of ADHD and associated research. This present research utilized bibliometric analysis to study the ADHD publications from India indexed in Scopus from 2014 to 2023.\u003c/p\u003e \u003cp\u003eBibliometrics, a globally adopted method, examines the impact of research output. Such analysis aids in identifying highly productive countries, organizations, resources, and authors within specific research fields. Concepts like Bradford's Law of Scattering(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) and Lotka's Law(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) help pinpoint core journals and author productivity. The increasing impact of scholarly resources reflects researchers' interest and the influence of research articles in a field (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNumerous mental health researchers use bibliometric methods and concepts to identify the progression and trends within various mental health domains. These encompass schizophrenia and inflammation (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), Neuroimaging in Psychiatric Disorders(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), Psychopathology and mental health (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), Perinatal anxiety(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), etc.\u003c/p\u003e \u003cp\u003eSeveral bibliometric studies have focused on ADHD. For instance, a group of researchers from medical institutes in China conducted a bibliometric study of 1975 articles on gut microbiota in ADHD (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The Web of Science was chosen for data retravel. The researchers identified the United States, China, and Spain as the leading countries with the most published articles. Additionally, the research looked at journal distribution, authorship pattern, co-citation analysis, and keyword analysis. The scientometric approach identified the major themes and trends in ADHD research during the last decade. 284381 publications collected from the Web of Science were analyzed(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The study results have identified four major research areas: 1) ADHD treatment, risk factors, and evidence synthesis; 2) neurophysiology, neuropsychology, and neuroimaging; 3) genetics; 4) comorbidities. A research paper published in the Annals of General Psychiatry analyzed the 100 highly cited articles on ADHD since 2014(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The researchers retrieved bibliographic information for the articles from PubMed. Their findings revealed that epidemiology emerged as the most popular field of study, with the United States providing the most significant contributions. Among the 100 highly cited publications, the Journal of the American Academy of Child and Adolescent Psychiatry had the highest number of research articles (15%).\u003c/p\u003e \u003cp\u003eIn additional studies, Denche-Zamorano and co-researcher used bibliometric approaches in psychometry to identify current research trends (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Similarly, Haiyin Deng and colleagues conducted a bibliometric analysis of ADHD to identify hotspots and developmental trends in neuroimaging during the last three decades (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese are the core objectives of the study:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo evaluate the development of research in ADHD.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo identify the most frequently selected source journals and their impact.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo pinpoint the most influential institute affiliations and authors.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo analyze keywords to identify recent research trends.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eThis Bibliometric analysis was performed using Scopus data. Scopus indexed 324131 documents on ADHD-related topics between 2014 and 2023. The publications from 2014 to 2023 from India associated with ADHD were selected for this analysis. A total of 721 indexed from India were searched and retrieved from March 22, 2024, on the advanced query terms \u003cem\u003eTITLE-ABS-KEY (Attention-deficit/hyperactivity disorder OR ADHD).\u003c/em\u003e Data analysis involves two steps. Firstly, the Scopus interface was utilized to analyze publication summaries and documents categorized by subjects and the most productive institution. Secondly, the bibliometrix R package will apply Bradford's Law to identify core journals and Lotka's Law to ascertain the most productive authors. The dataset was downloaded in .bib format for this analysis, and open-source software VOSviewer was utilized to visualize keyword analysis. Finally, the findings were organized into distinct categories\u003c/p\u003e \u003cp\u003e \u003c/p\u003e "},{"header":"RESULT AND DISCUSSION","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e1. Publication summary\u003c/h2\u003e\n \u003cp\u003eA thorough analysis of 718 papers from Scopus on ADHD encompassed 439 (61.1%) articles, 120 (16.7%) review articles, 61(9.6%) conference proceedings, 39 (5.4%) book chapters, 33(4.6%) letters to the editor, 8 (1.1%) editorials, and 10(1.5%) other types of contributions. All of the publications were published only in English. The annual growth of ADHD publications from India is shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. There were 31 documents from India in the initial year of 2014; this number significantly increased to 40 in 2015, 43 in 2016, and 45 in 2017. In 2018, there was a decrease in the growth trend, with 40 documents recorded. The number of documents increased significantly over the next five years, from 63 articles in 2019 to 163 in 2023. A substantial rise was observed, with 83 documents in 2021 and 163 in 2023. India stands out as one of the foremost contributors to ADHD research in Asia, securing fourth place and ranking 20th globally. Articles on ADHD from India showed a growing trend over the past decade, particularly since 2018. Most articles on ADHD published in India fall under the subjects of Medicine (36.1%), Psychology (10%), and Neuroscience (9%). ADHD research is critically important in India due to the country\u0026apos;s diverse population, cultural nuances, and unique socio-economic challenges. Early and effective management of ADHD can significantly impact children\u0026rsquo;s academic and social development. Research can help develop culturally appropriate diagnostic tools, interventions, and policies tailored to India\u0026rsquo;s needs. It also highlights the potential benefits of alternative therapies, making treatment more accessible and acceptable to the population (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e2. Document by subjects\u003c/h3\u003e\n\u003cp\u003eScopus indexed a total of 718 documents about ADHD across various disciplines (see Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). With the biggest share of 36.1%, medicine was the most popular field, followed by psychology (10%), neuroscience (9%), computer science (8.3%), and biochemistry (7.5%). The analysis indicates a significant use of artificial intelligence and computer science in mental health research, particularly in Indian studies on ADHD. This claim is supported by the document shares from engineering (5.8%) and computer science (8.3%). With an emphasis on ADHD research, these technologies have developed into vital tools for advancing mental health research. The seamless transition between computer science and artificial intelligence allows researchers to investigate creative methods and fixes, improving knowledge and tackling mental health issues (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003e3. Most productive institutions\u003c/h3\u003e\n\u003cp\u003eAround 165 institutes are actively engaged in contributing to ADHD research. Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the 15 most productive institutes. Notably, researchers and faculty associated with the National Institute of Mental Health and Neurosciences (NIMHANS) in Bangalore have emerged as the leading contributors, publishing 61 documents. As a premier mental health research institution and an \u0026ldquo;Institute of National Importance\u0026rdquo; recognized by the Government of India, NIMHANS\u0026apos;s significant role in ADHD studies is likely due to its robust infrastructure and expertise in mental health. Following closely is the All India Institute of Medical Sciences (AIIMS) in New Delhi with 39 publications and the Postgraduate Institute of Medical Education and Research (PGIMER) in Chandigarh with 29 documents. Three institutes stand out in terms of current research on ADHD: NIMHANS, AIIMS, and PGIMER. Between 2020 and 2023, NIMHANS released 29 papers on ADHD; in the same time frame, AIIMS Delhi produced 18 new papers. PGIMER has contributed fifteen new papers to the corpus of research on ADHD. Identifying such active institutions and productive authors is essential for fostering collaborations, advancing interdisciplinary research, and strengthening research networks.\u003c/p\u003e\n\u003cp\u003eBeyond these Indian institutions, foreign institutes have also contributed to recent ADHD research, indicating international collaboration. Noteworthy mentions include Harvard Medical School in the USA, contributing 15 documents; Karolinska Institute in Sweden, with 14 documents; Universidade Federal do Rio Grande do Sul in Brazil, publishing 13 documents; and the University of Cape Town, contributing 13. These international contributions highlight the collaborative efforts between Indian institutes and affiliated authors on the global stage (\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003e4. Core Journal\u003c/h3\u003e\n\u003cp\u003eThe articles on ADHD in India were disseminated across 165 different sources. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the top 15 highly productive sources, showcasing the number of documents, impact factor, and journal quartile. The impact factor, determined by Clarivate Analytics, serves as a crucial metric for assessing journal quality (\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e). It measures the frequency of citations received by selected articles in recent years. Additionally, the Scopus Quartiles offer a metric to evaluate a journal\u0026apos;s influence(\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e). These quartiles assess a journal\u0026apos;s citation performance relative to all other journals in the Scopus. They are classified into four categories: Q1 (green) represents the top quarter of journals with the highest values, Q2 (yellow) includes the second-highest values, Q3 (orange) encompasses the third-highest values, and Q4 (red) denotes the lowest values.\u003c/p\u003e\n\u003cp\u003eThe \u0026ldquo;Journal of the Indian Association for Child and Adolescent Mental Health\u0026rdquo; stands out with the highest number of documents (25) but holds the lowest impact factor of 0.6, placing it in Q4; this may be articles not addressing critical or globally relevant ADHD research topics, limited readership, indexing as a regional journal, or focusing on recent publications that haven\u0026apos;t had time to gain citations. On the other hand, the \u0026ldquo;Asian Journal of Psychiatry\u0026rdquo; has 22 documents, boasts the highest impact factor (9.5), and is classified in Q1. Most journals (n\u0026thinsp;=\u0026thinsp;11) are positioned in Q1 or Q2, indicating a significant influence within their core fields. Notably, the \u0026ldquo;Journal of Postgraduate Medicine\u0026rdquo; \u0026ldquo;The Journal of the Indian Association for Child and Adolescent Mental Health\u0026rdquo; \u0026ldquo;Advances in Intelligent Systems and Computing\u0026rdquo; and the \u0026ldquo;Indian Journal of Practical Pediatrics\u0026rdquo; exhibit the lowest values in this analysis. To gain a comprehensive understanding of the current state of research in ADHD and explore its potential future directions, identifying the most productive journals that consistently publish high-quality articles is crucial. Such journals are pivotal in disseminating groundbreaking research, advancing knowledge, and shaping clinical practices.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003emost productive journals\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource Title\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDocuments\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eImpact Factor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQuartiles\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJournal Of Indian Association for Child and Adolescent Mental Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsian Journal of Psychiatry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndian Journal of Psychiatry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndian Journal of Psychological Medicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndian Journal of Pediatrics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndian Pediatrics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJournal of Clinical Psychiatry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJournal of Attention Disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJournal of Postgraduate Medicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndian Journal of Medical Research\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResearch Journal of Pharmacy and Technology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdvances In Intelligent Systems and Computing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuropean Child and Adolescent Psychiatry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndian Journal of Practical Pediatrics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScientific Reports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e*TC\u0026thinsp;=\u0026thinsp;Total citation\u003c/p\u003e\n\u003cp\u003eCoined by Samuel Bradford in 1934, the Bradford Law of Scattering has been instrumental in identifying core journals within specific subject domains. Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the core journals identified through Bradford\u0026apos;s Law, with Zone 1 encompassing 21 journals. Notably, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e supports this, as the listed 15 most productive journals align with the core journals identified by Bradford\u0026apos;s Law.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e5. Most Influential Authors\u003c/h2\u003e\n \u003cp\u003eRegarding author-level analysis, the findings initially highlight the most productive authors, considering their impact on resources. Subsequently, they examine the authors\u0026apos; production trends over time and conclude by exploring the countries of the corresponding authors.\u003c/p\u003e\n \u003cp\u003eDuring the study period, 3393 authors contributed to publications related to ADHD; Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the top ten authors with their resource impact. Sinha S emerged as the most productive author, publishing 22 documents on ADHD and receiving 213 citations, resulting in an h-index of 9. Following closely is Mukhopadhyay K, with 21 publications on ADHD, garnering 168 citations and achieving an h-index of 8. Sagar S secured the third position with 19 publications, accumulating 557 citations and achieving an h-index of 8.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe most productive author with their resource impact\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAuthors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePublications\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eH Index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eG Iindex\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eM Index\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSINHA S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMUKHOPADHYAY K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSAGAR R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKARANDE S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.909\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSINGH A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eANDRADE C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.889\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBENEGAL V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMAITRA S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHATTERJEE M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJAISOORYA TS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTHENNARASU K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eARUN P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDE VRIES PJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGUPTA A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSHARMA A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e*TC\u0026thinsp;=\u0026thinsp;Total Citations\u003c/p\u003e\n\u003cp\u003eLotka\u0026apos;s Law, coined by Alfred J. Lotka (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e), describes the correlation between the number of authors and the number of articles within a specific subject domain. It suggests that a small group of highly productive authors will contribute a significant fraction of the published papers. In a sample illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, the application of Lotka\u0026apos;s Law reveals that 84.4% of authors contributed only one article each, while 8.7% published two papers on average. Additionally, approximately 3.3% of authors authored three papers, 1.5% contributed four publications each, and 0.5% published five papers. A smaller percentage of authors was also observed, ranging from those who contributed six to ten articles\u0026mdash;notably, only five authors published more than 15 articles each.\u003c/p\u003e\n\u003cp\u003eLotka\u0026apos;s Law asserts that a small fraction of remarkably productive writers will generate the bulk of publications within a given field. Conversely, most authors will contribute only a negligible number of papers individually.\u003c/p\u003e\n\u003ch3\u003e6. Highly cited article\u003c/h3\u003e\n\u003cp\u003eCitation analysis plays a significant role in bibliometric analysis by assessing the productivity of articles(\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e). A high number of citations indicates an article\u0026apos;s global and local influence within a research field. Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the top 10 highly cited articles, their source journals, and citation counts. These articles have an average of 150 citations each, with the first two being review articles and the third a meta-analysis. Notably, two systematic reviews and meta-analyses are included in this list. It\u0026apos;s worth mentioning that these highly cited articles are published in open-access journals, facilitating global recognition and free access to information.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ehighly cited articles.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTitle\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource Title\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCited by\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOA\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe World Federation of ADHD International Consensus Statement: 208 Evidence-based conclusions about the disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeuroscience and Biobehavioral Reviews\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrain charts for the human lifespan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePsychological and Behavioral Impact of Lockdown and Quarantine Measures for COVID-19 Pandemic on Children, Adolescents and Caregivers: A Systematic Review and Meta-Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJournal of Tropical Pediatrics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe burden of mental disorders across the states of India: the Global Burden of Disease Study 1990\u0026ndash;2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe Lancet Psychiatry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMethylphenidate for children and adolescents with attention deficit hyperactivity disorder (ADHD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCochrane Database of Systematic Reviews\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe global coverage of prevalence data for mental disorders in children and adolescents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEpidemiology and Psychiatric Sciences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePsychiatric disorders and obesity: A review of association studies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJournal of Postgraduate Medicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrevalence of comorbid psychiatric disorders among people with autism spectrum disorder: An umbrella review of systematic reviews and meta-analyses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePsychiatry Research\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMethylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of harmful effects in non-randomised studies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCochrane Database of Systematic Reviews\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhysiological and Functional Basis of Dopamine Receptors and Their Role in Neurogenesis: Possible Implication for Parkinson\u0026apos;s Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJournal of Experimental Neuroscience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7. Keyword analysis of ADHD research\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e illustrates the keyword analysis of ADHD-related documents published in India. In Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA, a network visualization depicts these keywords. Each node reflects a specific keyword, with node size indicating the frequency of occurrence. The distance separating two nodes signifies the intensity of their correlation. The keywords with closer distance were classified into the same cluster and reflected the core topics. Keywords with closer distances were grouped into the same cluster, representing core topics. Cluster 1 is highlighted in green, emphasizing key ADHD-related terms such as Human, Adolescent, India, Psychometry, Learning disabilities, and others.\u003c/p\u003e\n\u003cp\u003eIn Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB, an overlay visualization of keywords is depicted, with earlier appearances of keywords marked in blue and recent additions highlighted in yellow. Initially, prevalent topics included \u0026quot;Atomoxetine,\u0026quot; \u0026quot;DSM IV,\u0026quot; \u0026quot;Psychology,\u0026quot; \u0026quot;India,\u0026quot; \u0026quot;Genetic association,\u0026quot; and \u0026quot;Distress syndrome.\u0026quot; Conversely, recent years have witnessed increased interest in keywords such as \u0026quot;Microglia,\u0026quot; \u0026quot;autism spectrum disorder (ASD),\u0026quot; \u0026quot;Inflammation,\u0026quot; \u0026quot;Herbal medicine,\u0026quot; \u0026quot;Machine learning,\u0026quot; \u0026quot;Cocaine,\u0026quot; \u0026quot;Benzodiazepine,\u0026quot; and \u0026quot;Celiac disease.\u0026quot;\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC displays the top frequent keywords in ADHD research. \u0026quot;Human\u0026quot; emerges as the most frequently used author keyword, with 485 occurrences, followed by \u0026quot;male\u0026quot; with 417, \u0026quot;attention deficit hyperactivity disorders\u0026quot; with 373, and \u0026quot;adolescents\u0026quot; with 223.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e8. Major funding organization\u003c/h2\u003e\n \u003cp\u003eFunding plays a crucial role in supporting scientific research. Upon analyzing the financial sources of the 718 retrieved documents, it was found that 354 documents did not acknowledge any funding sources. However, funding support for the remaining 364 documents was provided by 159 different funding agencies from various countries.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the top 15 funding agencies, their respective countries, and the number of funded articles. Funding agencies from India provided financial support for 104 research studies. The Indian Council of Medical Research (ICMR) emerged as the most prolific funding agency, supporting 14 research studies over 10 years. Following closely behind, the University Grants Commission (UGC) sponsored 9 research studies. At the same time, the Council of Scientific and Industrial Research (CSIR) and the Department of Science and Technology, Ministry of Science and Technology, India, each supported 6 research studies.\u003c/p\u003e\n \u003cp\u003eAdditionally, the Department of Biotechnology, Ministry of Science and Technology, India, contributed to 5 research studies, rounding up the top 5 most productive funding agencies from India. Furthermore, agencies from the USA, Sweden, Brazil, Australia, Switzerland, and Belgium were featured in this list. Specifically, the USA supported 25 research studies, Sweden supported 21, Australia supported 19, Brazil supported 14, and Switzerland supported 9 research studies.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTop 15 funding organizations for ADHD research\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFunding organization\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo funded article\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndian Council of Medical Research\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNational Institutes of Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNational Institute of Mental Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUniversity Grants Commission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVetenskapsr\u0026aring;det- Swedish Research Council\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSweden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConselho Nacional de Desenvolvimento Cient\u0026iacute;fico e Tecnol\u0026oacute;gico (National Council for Scientific and Technological Development)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrazil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCouncil of Scientific and Industrial Research, India\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepartment of Science and Technology, Ministry of Science and Technology, India\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEli Lilly and Company\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNational Health and Medical Research Council\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAustralia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNovartis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSwitzerland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSvenska Forskningsr\u0026aring;det Formas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSweden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVINNOVA- Swedish Governmental Agency for Innovation Systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSweden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepartment of Biotechnology, Ministry of Science and Technology, India\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuropean Commission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBelgium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn conclusion, based on Scopus data, this bibliometric analysis offers a comprehensive overview of the Indian ADHD research landscape, highlighting publication trends, leading authors, productive journals, funding agencies, and key research areas. The structured findings serve as a valuable resource for the mental health research community and professionals, enabling them to identify emerging trends in ADHD research. Additionally, these insights can assist policymakers in shaping effective research funding strategies and supporting innovative studies while showcasing India\u0026rsquo;s contributions to ADHD research. For librarians and library professionals, this analysis aids in identifying high-impact journals and enhancing resource curation and accessibility.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCortese S, Sab\u0026eacute; M, Chen C, Perroud N, Solmi M (2022) Half a century of research on Attention-Deficit/Hyperactivity Disorder: A scientometric study. 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Children 9(12):1836. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/children9121836\u003c/span\u003e\u003cspan address=\"10.3390/children9121836\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng H, Huang Z, Li Z, Cao L, He Y, Sun N et al (2023) Systematic bibliometric and visualized analysis of research hotspots and trends in attention-deficit hyperactivity disorder neuroimaging. Front NeuroSci 17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnins.2023.1098526\u003c/span\u003e\u003cspan address=\"10.3389/fnins.2023.1098526\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuppili PP, Manohar H, Pattanayak RD, Sagar R, Bharadwaj B, Kandasamy P ADHD research in India: A narrative review. 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Eur Child Adolesc Psychiatry 17(6):381\u0026ndash;391. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00787-008-0680-1\u003c/span\u003e\u003cspan address=\"10.1007/s00787-008-0680-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMerig\u0026oacute; JM, Muller C, Modak NM, Laengle S (2019) Research in Production and Operations Management: A University-Based Bibliometric Analysis. Global J Flex Syst Manage 20(1):1\u0026ndash;29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40171-018-0201-0\u003c/span\u003e\u003cspan address=\"10.1007/s40171-018-0201-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaha S, Saint S, Christakis DA (2003) Impact factor: a valid measure of journal quality? J Med Libr Assoc 91(1):42\u0026ndash;46\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsan A, Aslan A (2020) Quartile Scores of Scientific Journals: Meaning, Importance and Usage. Acta Med Alanya 4(1):102\u0026ndash;108. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.30565/medalanya.653661\u003c/span\u003e\u003cspan address=\"10.30565/medalanya.653661\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoyons ECM, Moed HF, Luwel M (1999) Combining mapping and citation analysis for evaluative bibliometric purposes: A bibliometric study. J Am Soc Inform Sci 50(2):115\u0026ndash;131. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/(SICI)1097-4571(1999)50:2\u003c/span\u003e\u003cspan address=\"10.1002/(SICI)1097-4571(1999)50:2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Institute of Mental Health and Neurosciences","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Attention-Deficit Hyperactivity Disorder, Bibliometric, Publication Growth, Research Trends, Bibliometrix R","lastPublishedDoi":"10.21203/rs.3.rs-5888968/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5888968/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction:\u003c/strong\u003e Attention-Deficit/Hyperactivity Disorder (ADHD) is a widely studied neurodevelopmental disorder with increasing research contributions from India. This study examines the impact of Indian ADHD research, focusing on publication growth, research trends, and resource impact. Using bibliometric methods, it identifies key institutions, authors, funding agencies, and current research trends shaping the field.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Data for this study were sourced from the Scopus bibliographic database, retrieving 721 documents affiliated with India. Bibliometric analysis was conducted using the bibliometrix R package. Bradford's Law was applied to identify core journals, Lotka's Law was used to assess author productivity, and keyword analysis was used to determine prevalent research themes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e India is a leading contributor to ADHD research, with the highest publication count in 2023. Approximately 36.1% of research publications on ADHD are within medicine. The National Institute of Mental Health and Neurosciences (NIMHANS) emerged as the most productive institution, while Prof.Sinha was identified as the most prolific author. The Indian Council of Medical Research (ICMR) was the top funding agency. Bradford’s Law revealed 21 core journals publishing ADHD research, and keyword analysis highlighted contemporary research themes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e This study highlights India's significant role in ADHD research, driven by contributions from key institutions, prolific authors, and robust funding. Identifying core journals and emerging research trends offers valuable insights for future research directions in this field.\u003c/p\u003e","manuscriptTitle":"Trends and Growth in ADHD Research: A Bibliometric Analysis of Indian Literature Indexed in Scopus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-24 17:33:03","doi":"10.21203/rs.3.rs-5888968/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8a1902f0-85e4-4976-9ea9-0a5ea9349e22","owner":[],"postedDate":"January 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43317397,"name":"Library Science"},{"id":43317398,"name":"Psychiatry"}],"tags":[],"updatedAt":"2025-01-24T17:33:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-24 17:33:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5888968","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5888968","identity":"rs-5888968","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}