Word cloud analysis of the <i>BJGP</i>: 5 years on

Atenstaedt, Robert

doi:10.3399/bjgp17x690833

Cited by 43 publications

(32 citation statements)

References 2 publications

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“…2 [ 25 ]. Through viewing the text colors, it is easy to discriminate the countries (red), MeSH terms (green), and article types (blue), which is different from traditional word clouds [ 26 ], only displaying one entity(or attribute) in a picture. The top three articles are linked by the triangle lines at the top-left corner in Fig.…”

Section: Resultsmentioning

confidence: 99%

Predicting the number of article citations in the field of attention-deficit/hyperactivity disorder (ADHD) with the 100 top-cited articles since 2014: a bibliometric analysis

2021

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Background Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children or early adolescents with an estimated worldwide prevalence of 7.2%. Numerous articles related to ADHD have been published in the literature. However, which articles had ultimate influence is still unknown, and what factors affect the number of article citations remains unclear as well. This bibliometric analysis (1) visualizes the prominent entities with 1 picture using the top 100 most-cited articles, and (2) investigates whether medical subject headings (i.e., MeSH terms) can be used in predicting article citations. Methods By searching the PubMed Central® (PMC) database, the top 100 most-cited abstracts relevant to ADHD since 2014 were downloaded. Citation rank analysis was performed to compare the dominant roles of article types and topic categories using the pyramid plot. Social network analysis (SNA) was performed to highlight prominent entities for providing a quick look at the study result. The authors examined the MeSH prediction effect on article citations using its correlation coefficients (CC). Results The most frequent article types and topic categories were research support by institutes (56%) and epidemiology (28%). The most productive countries were the United States (42%), followed by the United Kingdom (13%), Germany (9%), and the Netherlands (9%). Most articles were published in the Journal of the American Academy of Child and Adolescent Psychiatry (15%) and JAMA Psychiatry (9%). MeSH terms were evident in prediction power on the number of article citations (correlation coefficient = 0.39; t = 4.1; n = 94; 6 articles were excluded because they do not have MeSH terms). Conclusions The breakthrough was made by developing 1 dashboard to display 100 top-cited articles on ADHD. MeSH terms can be used in predicting article citations on ADHD. These visualizations of the top 100 most-cited articles could be applied to future academic pursuits and other academic disciplines.

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Section: Resultsmentioning

confidence: 99%

Predicting the number of article citations in the field of attention-deficit/hyperactivity disorder (ADHD) with the 100 top-cited articles since 2014: a bibliometric analysis

2021

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“…Relevant entities regarding article types provide insights into the overall concept when presented on a visual board, which was hard to display using the traditional word cloud technique. [ 31 ] A Sankey diagram was particularly used to interpret the association of the most dominant entities in the network.…”

Section: Methodsmentioning

confidence: 99%

Predicting article citations using data of 100 top-cited publications in the journal Medicine since 2011

Kuo

Chien²,

Kuo

et al. 2020

Medicine

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“…Even if a word was already selected by a fellow student, it was not mandatory for the next student to change his or her own selection. Combination word concepts such as ‘neuro‐developmental’, ‘cerebral palsy’, ‘hand hygiene’, ‘hand washing’, ‘high‐risk’ and ‘breast milk’, were all entered as single words, as the WC generator cannot recognise semantics when such words are split up . Words with the same meaning and expressed differently were pooled (e.g.…”

Section: Methodsmentioning

confidence: 99%

“…A word cloud (WC) is a visual representation of a list or series of words and the size of the printed words in a WC is based on the frequency of words; the more a word is mentioned, the larger it will appear in the WC . WC is considered as an invaluable tool to quantify the frequency with which a given word is repeated, thus identifying the focus of the written material . WC generated from spontaneously expressed views by medical students could assist the teaching faculty in analysing students' perceptions, biases, apprehensions, expectations and personal motivation, thus acting as a learning tool fostering better engagement and reflective feedback.…”

Section: What Is Already Known On This Topicmentioning

confidence: 99%

Word cloud analysis and single word summarisation as a new paediatric educational tool: Results of a neonatal application

Philip

2020

J Paediatrics Child Health

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Aims To analyse the value of computerised ‘word cloud’ (WC) generated from spontaneously articulated ‘single word summarisation’ (SWS) by medical students to assist their learning during the neonatal intensive care unit placement. To highlight WC as a potential new tool in paediatric teaching, improving student engagement and reflective feedback. Methods An observational study was conducted in the neonatal intensive care unit of University Maternity Hospital Limerick, Ireland for 5 years (October 2012 to September 2017). One faculty member prospectively recorded SWS in neonatology by graduate entry medical students. An online ‘WC generator’ under an open‐source licence was used to compute the WC. Hospital audit committee approved the study. Results A total of 268 SWS were recorded consecutively from 268 medical students towards the WC generation. Structured multi‐response student feedback showed SWS and WC as stimulating, unique and creative. Powerpoint presentation of the computed WC prompted students to reflect on their chosen words and that of peers. Conclusions Visualisation of medical student‐generated SWS as a WC could stimulate interaction, reflection and clinical discussion, thus assisting teachers to foster better student engagement. This innovative educational tool equips students to ‘convey more with fewer words’ and has the potential transferability to other clinical disciplines.

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Word cloud analysis of the BJGP: 5 years on

Cited by 43 publications

References 2 publications

Predicting the number of article citations in the field of attention-deficit/hyperactivity disorder (ADHD) with the 100 top-cited articles since 2014: a bibliometric analysis

Predicting the number of article citations in the field of attention-deficit/hyperactivity disorder (ADHD) with the 100 top-cited articles since 2014: a bibliometric analysis

Predicting article citations using data of 100 top-cited publications in the journal Medicine since 2011

Word cloud analysis and single word summarisation as a new paediatric educational tool: Results of a neonatal application

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