- Email: [email protected]
Using the Kano model to display the most cited authors and affiliated countries in schizophrenia research
Schizophrenia Research xxx (xxxx) xxx
Contents lists available at ScienceDirect
Schizophrenia Research journal homepage: www.elsevier.com/locate/schres
Using the Kano model to display the most cited authors and affiliated countries in schizophrenia research Chien-Ho Lin a, Po-Hsin Chou b, c, Willy Chou d, Tsair-Wei Chien e, * a
Department of Psychiatry, Chi Mei Medical Center, Taiwan School of Medicine, National Yang-Ming University, Taipei, Taiwan c Department of Orthopedics and Traumatology, Taipei Veterans General Hospital, Taipei, Taiwan d Department of Physical Medicine and Rehabilitation, Chi Mei Medical Center, Taiwan e Department of Medical Research, Chi Mei Medical Center, Taiwan b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 20 October 2018 Received in revised form 26 October 2019 Accepted 30 October 2019 Available online xxx
In order to improve individual research achievements (IRA), this study investigates which affiliated countries and authors earn the most cited IRAs and whether those types of articles are associated with the number of cited papers on schizophrenia from a leading journal in the field. The Kano model was used for displaying the IRAs. Clusters of medical subject headings (MeSH) were applied to explore the core concepts of a given journal. This study aimed to apply social network analysis (SNA) and an authorship-weighted scheme (AWS) to inspect the association between MeSH terms and IRA. About 2,008 abstracts published between 2012 and 2016 in the journal Schizophrenia Research were downloaded from Pubmed Central using the keyword (Schizophr Res)[Journal] on September 20, 2018. The MeSH terms were clustered by using SNA to separate the core concepts and compare the differences in bibliometric indices (i.e., h, Ag, x and author impact factor or AIF). Visual dashboards were shown on Google Maps. Results indicate that (1) the US, the UK, and Canada earn the highest x-index; (2) the top one author from the US has the highest x-index (¼ 5.73 with x-core at cited ¼ 16.44 and citable ¼ 2); (3) the article type of schizophrenic psychology shows distinctly higher frequencies than others; and (4) article types are associated with the number of cited papers. Four approaches of the Kano model, SNA, MeSH terms, and AWS can be accommodated to display IRAs, classify article types, and quantify coauthor contributions in the article byline, respectively, and applied to other scientific disciplines in the future, not just in this specific journal. © 2019 Elsevier B.V. All rights reserved.
Keywords: Schizophrenia Social network analysis Google maps Pubmed central Authorship-weighted scheme Individual academic achievement
1. Introduction The mental disorder schizophrenia affects over 21 million people worldwide (WHO, 2016; Chien et al., 2018a,b,c), with a lifetime prevalence rate of 1% in the general population (Wu and Duan, 2015). The top 10 journals with the highest numbers of publications on schizophrenia have an Impact Factor of 2 or higher. In this ranking, Schizophrenia Research (SR for short) is at the top, with the most articles published on schizophrenia (329, 35.26%)(Chien et al., 2018a,b,c), and a journal impact factor (JIF) of 3.986 in 2017 (JCR, 2019). In relation to these, the authors (or the article types) who
* Corresponding author. Taipei Veterans General Hospital, 18F, 201, Section 2, Shipai Road, Beitou District, Taipei,112, Taiwan. E-mail addresses: [email protected] (C.-H. Lin), [email protected] (P.-H. Chou), [email protected] (W. Chou), [email protected] (T.-W. Chien).
contributed most to the JIF for SR and the article types associated with the number of cited papers in SR remain unknown. Many papers (Chien et al., 2018a,b,c; Fulginiti et al., 2016; Wu and Duan, 2015) investigated author collaborations in schizophrenia using social network analysis (SNA). Medical Subject Headings (MeSH) is a useful way for classifying the scope(s) of a given journal(Minguet et al., 2017) (or, core concepts in this study), similar to the unsupervised training procedure in machine learning(Buehler and Rashidi, 2005; Rashidi, 2019), by differentiating the patterns of article MeSH terms. As such, the connections among these MeSH terms are worthy of using SNA(Chien et al., 2018a,b,c; Chien et al., 2018; Lu et al., 2017) based on the co-occurrence phenomena. However, to date, no other matching scheme has been applied to scientific disciplines for determining which article types contribute the most in improving the JIF for a given journal. The prediction of article types related to the number of citations can be verified if the article types (e.g., using MeSH terms) have been
https://doi.org/10.1016/j.schres.2019.10.058 0920-9964/© 2019 Elsevier B.V. All rights reserved.
Please cite this article as: Lin, C.-H et al., Using the Kano model to display the most cited authors and affiliated countries in schizophrenia research, Schizophrenia Research, https://doi.org/10.1016/j.schres.2019.10.058
2
C.-H. Lin et al. / Schizophrenia Research xxx (xxxx) xxx
properly classified. Every June, millions of academic scholars pay close attention to the Journal Citation Reports (JCR) ranking of the JIF for each indexed journal. However, no author IFs (AIFs) (Lippi and Mattiuzzi, 2017; Pan and Fortunato, 2014) or bibliometric indices (Hirsch, 2005; Egghe, 2006; Fenner et al., 2018) have gained scientists’ or scholars’ attention as much as JIF does. The reasons for this might be the lack of a simple 5-year moving average method(Pan, 2014) that can be along with an established authorship-weighted scheme (AWS)(Chien et al., 2018a,b,c; Sekercioglu, 2008; Vavry cuk, 2018) for tracking the dynamics of individual scientific impact and quantifying the coauthor contributions. Many counting schemes for quantifying coauthor contributions(Sekercioglu, 2008) (e.g., fractional counting (Batista et al., 2006; Egghe et al., 2000; Lindsey, 1982; Tscharntke et al., 2007; Waltman, 2015) and authorship-weighted counting(Egghe et al., 2000; Tscharntke et al., 2007; Waltman, 2015) have been proposed as an alternative to the traditional full counting (i.e., all authors contributed equally to an article). Thus far, we have not seen any articles completely applying the AWS to any academic discipline in the past. In the current study, we attempt to (1) quantify coauthor contributions with AWS to present the most cited affiliated countries and authors using a Kano diagram (Kano et al., 1984) to display, and (2) inspect the association between MeSH terms and the individual research achievements (IRA).
The sum of author weights in a byline ¼
m1 X k¼0
expðgj Þ Pm1 j ¼ 0 expðgj Þ (3)
The sum of authorships equals 1 for each paper referred to Eq. (3). This is a basic concept that ensures that all papers have an equal weight irrespective of the number of co-authors(Vavry cuk, 2018). Accordingly, more importance is given to the first (¼ exp(m-1), primary) and the last (¼ exp(m-2), corresponding or supervisory) authors, and it is assumed that the others (the middle authors) have made smaller contributions (Egghe et al., 2000). In Eq. (3), the smallest portion (¼ exp(0) ¼ 1) is assigned to the last second author with the odds ¼ 1 as the basic reference. 2.2.2. Using x-index to display IRA on maps All authors’ citation numbers for each article are displayed in descending order along with the ascending sequential integral number(i) from 1 to n(¼the number of publications). The x-indexes qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi [ ¼ maxði ci Þ, where all the number of cited papers are denoted i
by ci] based on the x-core publications at i(Fenner et al., 2018), were applied to obtain the research achievements for affiliated counties and authors, respectively. The choropleth maps (Chien et al., 2019; Barcelona Field Studies Centre, 2019; Cromley and Ye, 2006; Cromley, Cromley, 2009) and the Kano diagram (Kano et al., 1984) were used for displaying the IRAs, see Additional File 3.
2. Methods 2.3. Objective 2: Inspecting the association between MeSH terms and the IRA
2.1. Data source We searched the database in the Pubmed Center (PMC) using the keyword (Schizophr Res)[Journal] on September 20, 2018. A total of 2,008 abstracts published between 2012 and 2016 were identified. We created a Microsoft Excel VBA (visual basic for application) module to handle the data, see Additional Files 1 and 2. All downloaded abstracts met the requirement for the type of journal article. Ethical approval was not necessary for this study, as no human subjects or personal data were accessed. 2.2. Objective 1: Using AWS to present the most cited entities on a Kano diagram 2.2.1. The author-based perspective The AWS and the AIF calculations are shown in Equations (1) and (2) (Chien et al., 2018a,b,c):
expðgj Þ 2:72gj ¼ Pm1 Wj ¼ Pm1 ; gj j ¼ 0 expðgj Þ j ¼ 0 2:72
P AIF ¼
(1)
2.3.1. Using social network analysis to cluster MeSH terms Social network analysis (SNA) (Chien et al., 2018a,b,c; Chien et al., 2018; Lu et al., 2017) helps explore the pattern of entities in a network. Pajek (Batagelj and Mrvar, 2003; deNooy et al., 2011; Minguet et al., 2017) is a widely used SNA program(Chien et al., 2018a,b,c). In keeping with the Pajek guidelines, we defined the MESH term as a node connecting to other nodes through the edge. Usually, the weight between the two nodes is determined by the number of connections. Centrality is a vital index to analyze the network. Any individual or keyword that lies in the center of the social network can determine its influence on the system and its ability to gain information(Han, 2017). We applied betweenness and degree centrality for selecting the most influential MeSH terms. The algorithm of community partition was applied to identify and separate clusters. 2.3.2. Citations on MeSH terms Similar to the IRA for authors, all weights for each MeSH term in an article are equal (i.e., the power gj equals zero for each MeSH
Cited:papers:based:on: Wj :in:a:given:year:and:the:proceeding:5:yrs Citable:papers: Wj :in:the:given:5:yrs
Considering a paper of mþ1 authors with the last being the corresponding author, Wj in Eq. (1) denotes the weight for an author on the order j in the article byline. The power gj is an integer number from m-1 to 0 in descending order.
(2)
term in Eq. (1) or Wj ¼ 1/n, n denotes for the number of MeSH terms in the article). The bibliometric indices (i.e., h, g, x (Hirsch, 2005; Egghe, 2006; Fenner et al., 2018) and AIF (Lippi and Mattiuzzi, 2017; Pan and Fortunato, 2014) on each MeSH term were computed according to their core citations and publications. The highest MeSH term in their own cluster stands for the article type.
Please cite this article as: Lin, C.-H et al., Using the Kano model to display the most cited authors and affiliated countries in schizophrenia research, Schizophrenia Research, https://doi.org/10.1016/j.schres.2019.10.058
C.-H. Lin et al. / Schizophrenia Research xxx (xxxx) xxx
3
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pg h(ci ), g( i¼1 ci /g), x-indexes[ ¼ maxði ci Þ, AIF in Eq. (2), and i
the Ag defined by accumulative citations and the number of core Pg articles at g as c =g to improve the IRA discrimination in i¼1 i ranking comparison (Huang and Chi, 2010). The study flowchart is shown in Fig. 1. 3. Results 3.1. The most cited affiliated countries The top three most cited affiliated countries are the US, the UK, and Canada; see Fig. 2. The most publication outputs in SR from 2012 to 2016 came from the U.S. (686, 34.16%). The UK and Brazil present an increasing trend (0.72 and 0.63, respectively), as shown in the last column in Table 1. The US has earned the highest x-index (35.95) over the past five years in SR. Relatively, SR has gained an xindex with 54.44 points. The Kano diagram separates all countries into three parts (i.e., the citation- originated excitement, the one-dimension performance, and the basic publication-originated requirement). Only the US is classified as the publication-originated requirement at the bottom right in Fig. 3. 3.2. The most cited authors Fig. 1. Study flowchart.
2.3.3. Comparison of article types in metrics Four bibliometric indices for the representative MeSH term for the top 9 clusters were computed using the following formulas:
The author Gregory P. Strauss from the US has the highest xindex (¼ 5.73 with x-core at cited ¼ 16.44 and citable ¼ 2); see Fig. 4. The author Jason Schiffman from the US published seven articles in SR from 2012 to 2016 with x-index ¼ 2.36, Cited ¼ 0.7, and Citable ¼ 8; see the right-bottom corner in Fig. 4. Interested readers are encouraged to scan the QR code on the Figure to see more information about the authors on the Kano diagram.
Fig. 2. The most cited countries around the world in SR.
Please cite this article as: Lin, C.-H et al., Using the Kano model to display the most cited authors and affiliated countries in schizophrenia research, Schizophrenia Research, https://doi.org/10.1016/j.schres.2019.10.058
4
C.-H. Lin et al. / Schizophrenia Research xxx (xxxx) xxx
Table 1 Distribution of publication outputs across continents and over the years. Continent
2012
2013
2014
2015
2016
Total
%
Growth
AFRICA South Africa Ethiopia Others ASIA China Japan Taiwan Israel Hong Kong Others EUROPE U.K. Germany Spain Netherlands France Others N. AMERICA U.S. Canada Mexico Cuba OCEANIA Australia New Zealand Aruba S. AMERICA Brazil Argentina Others Total
2 1 1
4 2
4 3 1
3 1 1 1 67 17 12 9 4 4 21 148 24 22 18 9 16 59 202 163 34 4 1 26 25 1
1 1
54 12 18 5 3 5 11 135 36 17 20 12 8 42 130 104 25 1
26 20 3 3 472
13 10 2 1 345
14 8 3 3 313 77 74 36 29 28 69 687 137 91 87 82 56 234 833 686 135 10 2 95 90 4 1 66 54 6 6 2008
0.70 0.40 0.15 0.15 15.59 3.83 3.69 1.79 1.44 1.39 3.44 34.21 6.82 4.53 4.33 4.08 2.79 11.65 41.48 34.16 6.72 0.50 0.10 4.73 4.48 0.20 0.05 3.29 2.69 0.30 0.30 100.00
0.36 0.18 0.29 0.18 0.17 0.18 0.44 0.46 0.94 0.69 0.44 0.49 0.72 0.28 0.20 0.65 0.36 0.43 0.14 0.30 0.49 0.40 0.29 0.09 0.03 0.71 0.00 0.65 0.63 0.61 0.61 0.16
49 12 8 8 10 6 5 115 22 18 13 18 5 39 153 130 23
2 65 8 19 9 6 7 16 146 22 16 25 29 11 43 181 156 23 2
17 16 1
19 18 1
6 5 1
7 7
78 28 17 5 6 6 16 143 33 18 11 14 16 51 167 133 30 3 1 21 19 1 1 14 12
342
422
2 427
12 12
x-index
3.08
11.4 8.63 6.56 5.79
14.57 11.5 9.94 11.79 7.29
35.95 13.11 3.24 1.12 10.67 1.41 7.11 2.75 54.44
Fig. 3. Using the x-index to display the most cited countries on the Kano diagram.
3.3. Clusters of MeSH terms The top nine MeSH clusters are presented in Fig. 5, and the representative terms with the most influential betweenness centrality are shown for each cluster. The bigger one is that of “schizophrenic psychology.” Interested readers are encouraged to scan the QR code on Fig. 5 to see more details in the PMC by clicking “publication” on the map when the specific MeSH bubble is selected.
Fig. 4. Using the x-index to display the most cited authors on the Kano diagram.
higher metric (averaged by Ag, x, and h) than others. 4. Discussion 4.1. Principal findings
3.4. Comparison of article types in metrics One-way ANOVA was performed to present the article types that are statistically distinct [F(20,8) ¼ 13.46, p < 0.001] in SR (Fig. 6). We can see that the core concept of “schizophrenic psychology” has a
Results indicate that (1) the US, the UK, and Canada earn the highest x-index; (2) author Gregory P. Strauss from the US has the highest x-index (¼ 5.73 with x-core at cited ¼ 16.44 and citable ¼ 2); (3) the article type of schizophrenic psychology shows
Please cite this article as: Lin, C.-H et al., Using the Kano model to display the most cited authors and affiliated countries in schizophrenia research, Schizophrenia Research, https://doi.org/10.1016/j.schres.2019.10.058
C.-H. Lin et al. / Schizophrenia Research xxx (xxxx) xxx
5
Fig. 5. Cluster analysis of MeSH terms using betweenness centrality for Schizophr Res.
2011), all of which are classified by subjective human judgments. We applied SNA to classify MeSH terms and then assigned articles into an appropriate cluster, through which the bibliometric metrics for each article type can be computed by the respective number of article citations and publications. To the best of our knowledge, no paper has adopted this objective way of analyzing the relationship between MeSH terms and citation metrics as we did in this study, though MeSH terms for characterizing journal features have been applied to articles(Balogh et al., 2019; Chien et al., 2018a,b,c; Salgado and Fernandez-Llimos, 2019; Yang and Lee, 2018).
Fig. 6. Comparison of bibliometric indices among clusters.
distinctly higher frequencies than others; and (4) article types are associated with the number of cited papers. The concept of “schizophrenic psychology” has significantly higher bibliometric metrics compared to other counterparts [F(20,8) ¼ 23.36, p < 0.001]; see Fig. 6. There are three features in this study, as indicated in the following: (1) classifying the type of articles (or core concept) using MeSH terms, (2) quantifying the coauthor contributions with AWS, and (3) demonstrating the affiliated countries and authors that have the highest research achievements in SR as shown on Google Maps. 4.2. Features and implications in this study 4.2.1. Classifying article types Traditionally, articles are classified based on the types of (i) case reports, reviews, and original articles (Bhandari et al., 2004; Rodríguez-Lago et al., 2018), (ii) systematic reviews and other systematic or narrative reviews (Alotaibi et al., 2016; Thulesius, 2011), and (iii) methodology and design or others(Thulesius,
4.2.2. Quantifying coauthor contributions with AWS Although the h-index(Hirsch, 2005) and others (Egghe, 2006; Fenner et al., 2018; Lippi and Mattiuzzi, 2017; Pan and Fortunato, 2014) have been suggested for measuring IRA in scientific disciplines, one of their shortcomings is the assumption of equal credits for all co-authors in an article(Chien et al., 2018a,b,c; Sekercioglu, 2008; Vavry cuk, 2018). Many formulas have been proposed in the literature((Batista et al., 2006; Egghe et al., 2000; Lindsey, 1982; Tscharntke et al., 2007; Waltman, 2015; Egghe et al., 2000; Tscharntke et al., 2007; Waltman, 2015). As of publication, we have yet to encounter an empirical study that can solve the problem of quantifying co-author contributions(Sekercioglu, 2008) in academics. 4.2.3. Entities on the Kano diagram using Google Maps to display We show our resulting dashboard on Google Maps, which allows readers to examine the details of the information by manipulating the zoom-in and zoom-out functionalities. This is a better representation than the traditional ones, which used static images in an article. In particular, both the choropleth map and the Kano diagram are present in Figs. 2e4, which make it easy for readers to visualize representations and to catch the highlight results of interest. 4.2.4. MeSH terms used for representing article types Compared to previous studies (Bhandari et al., 2004; Nielsen
Please cite this article as: Lin, C.-H et al., Using the Kano model to display the most cited authors and affiliated countries in schizophrenia research, Schizophrenia Research, https://doi.org/10.1016/j.schres.2019.10.058
6
C.-H. Lin et al. / Schizophrenia Research xxx (xxxx) xxx
and Seitz, 2016; Rodríguez-Lago et al., 2018), this study reports (1) a higher impact factor being associated with the publication of reviews and original articles instead of those case reports, (2) rigorous systematic reviews receiving more citations than other narrative reviews, and (3) case reports with low IFs due to their infrequent citation by articles. The MeSH clusters is a new approach and is markedly different from previous studies in that it can objectively verify the core concepts of an article for a given journal. As such, the bibliometric metrics can be obtained if each article has been assigned to the corresponding type. 4.2.5. The reason for using x-index as an IRA metric The reason we prefer the x-index to the h-index for demonstration purposes in Figs. 2e4 is explained by the following three extreme examples(Fenner et al., 2018): if one author has a single publication with 100 citations and another has ten publications each with ten citations, then the h-index of the former is one, while the hindex of the latter is 10. At the other extreme, an author with 100 publications, each with a single citation, has an h-index of 1. If the xindex is applied, all those examples mentioned above would reach an identical value of 10 for measuring the IRA. Alternatively, the gindexes are similar to the h-indexes at 1, 10, and 1. The Ag-indexes are located at 100, 10, and 1. Hence, the x-index can reasonably be used to discriminate IRA on axis Y in Fig. 5 and Ag on axis X. In addition, the x-index is suitable for using the Kano diagram to examine whether the entities are citation- or publication-oriented requirements in attributes. There are many other bibliometric indices, such as the R-index qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffi Ph P (¼ Ah, where A ¼ hi¼1 ci =h)(BiHui et al., 2007), and i¼1 ci ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Ph 2 the Euclidean-index(¼ i¼1 ci )(Perry and Reny, 2016), each with their own sets of features and limitations. All of those indices would have high correlations with one another in comparison to the AIF. The reasons for JCR preferring JIF to metrics can be seen from the results of this study, which reveal that (1) metrics are easily inflated by the number of publications, and (2) the JIF penalizes the journal with a high quantity of outputs. It is often easier for journals than for individuals to manipulate publication output. 4.2.6. Few studies using PMC citations to explore IRAs Another notable feature is the PMC citations used in this study. Over 100 papers were found by searching for the “most-cited articles” [Title] in the Pubmed library on October 10, 2018. Most of them have applied academic databases to conduct the citation analysis, such as the Scientific Citation Index (SCI; Thomson Reuters, New York, NY, the United States), Scopus (Elsevier, Amsterdam, the Netherlands), and Google Scholar (Alotaibi et al., 2016; Thulesius, 2011), to investigate the most-cited articles in a specific discipline. Only two(Chien et al., 2018/2019) download citation records from PubMed library (i.e., a free search engine accessing the PMC database of references and abstracts of papers on life sciences and biomedical topics). Using Pubmed citations to download citations and analyze the association between article types and the number of cited papers is worthy of further investigation in the future.
same name or use the same abbreviations but are affiliated with different institutions. Third, using MeSH terms to define the article type is arbitrary. The concept should be inducted from all, or at least two or three, main elements instead of the principal one. For example, schizophrenic psychology is related to complications and diagnosis; drug therapy is close to therapeutic use and drug effects, and metabolism is associated with pathology and enzymology. Interested readers are encouraged to scan the QR-code on Fig. 2 to examine more relevant MeSH terms in a cluster to define the true concept for the latent cluster. Fourth, although our cluster analysis and the AWS formula are useful approaches for verifying the association of MeSH terms and the number of weighted cited papers for individual authors, the results may be affected by the accuracy of MeSH terms and the authors’ actual contributions instead of the last as the true corresponding author. We used a variety of methods for cleaning and identifying data in this research, but some errors still exist, and these may affect the cluster results to some extent. Fifth, the MeSH terms clustered by the social network analysis are merely defined by the representative with the highest centrality degree in the respective cluster, which is arbitrary enough to bias the resulting and the inference making. Although the unsupervised training procedure in machine learning (Buehler and Rashidi, 2005; Rashidi, 2019) has been applied to differentiate the patterns of article MeSH terms using SNA in this study, the article types should be defiend more cautious not only in this study but also in the future. Finally, some terms in Fig. 5 are unfamiliar to psychiatrists in clinical settings, like schizophrenic psychology and physiopathology which were classified and named by the MeSH team in PMC. The term of schizophrenic psychology should be psychopathology or symptomatology in terminologies. Similarly, the term of psysiopathology should be pathophysiology that is used in scientific communication in psychiatry and psychology diagnostic classification. Furthermore, many researchers believe that the impact factor or citation does not reflect the scientific quality of the research work(Callaway, 2019; Journal Editorial, 2016; Seglen, 1997). Many journals are now preferring citation distribution curve over JIF(Callaway, 2019). Authors can discuss how this method for author and journal analyses can be included. Whether the x-index(Fenner et al., 2018) that is applied in this study can be used for journal research achievements is worthy of further studies and discussions in the future. 5. Conclusions By the above results and discussion, some valuable results are obtained, including the article types of a scholarly journal associated with the number of the cited metrics. Results suggest that both the methods of classifying article types and quantifying coauthor contributions might be applied to other scientific disciplines. As such, which type of articles and who contributes most to a specific journal can be evaluated by scientists and scholars in the future.
4.3. Limitations
Ethics approval and consent to participate
Although the findings are based on the above analysis, there are still several potential limitations that may encourage further research efforts. First, this study only focuses on one journal, so it cannot be generalized to other fields, particularly those with different characteristics and science categories. Second, there might be some biases in author identification because some authors in the bibliometric database may have the
Not applicable. All data were downloaded from the MEDLINE database at pubmed.com. Funding There are no sources of funding to be declared.
Please cite this article as: Lin, C.-H et al., Using the Kano model to display the most cited authors and affiliated countries in schizophrenia research, Schizophrenia Research, https://doi.org/10.1016/j.schres.2019.10.058
C.-H. Lin et al. / Schizophrenia Research xxx (xxxx) xxx
Authors’ contributions CH conceived and designed the study, PH and WC interpreted the data, and TW monitored the process. CH drafted the manuscript. All authors read the paper and approved the final manuscript. Authors’ information CH and PH are MD and assistant professor, Both WC and TW are associated professors at ChiMei Medical Center, Taiwan. Consent to publish Not applicable. Availability of data and materials All data used in this study are available in Additional files. Declaration of competing interest The authors declare that they have no competing interests. Acknowledgments We thank Enago (www.enago.tw) for the English language review of this manuscript and the fundng source from Chi Mei Medical Center (Taiwan) in 2017 (ID¼CMFHR10627). All authors declare no conflicts of interest. List of abbreviations AIF AWS IRA JIF MeSH PMC SNA SR
author impact factor authorship-weighted scheme individual research achievements journal impact factor medical subject headings Pubmed Central social network analysis Schizophrenia Research
Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.schres.2019.10.058. References Alotaibi, N.M., Nassiri, F., Badhiwala, J.H., Witiw, C.D., Ibrahim, G.M., Macdonald, R.,L., Lozano, A.M., 2016. The most cited works in aneurysmal subarachnoid hemorrhage: a bibliometric analysis of the 100 most cited art. World Neurosurg 89, 587e592 e6. Balogh, S.G., Zagyva, D., Pollner, P., Palla, G., 2019. Time evolution of the hierarchical networks between PubMed MeSH terms. PLoS One 14 (8), e0220648. Batagelj, V., Mrvar, A., 2003. Pajek - analysis, and visualization of large networks. In: inJünger, M., Mutzel, P. (Eds.), Graph Drawing Software. Springer, Berlin, pp. 77e103. Batista, P.D., Campiteli, M.G., Kinouchi, O., 2006. Is it possible to compare researchers with different scientific interests? Scientometrics 68 (1), 179e189. Bhandari, M., Montori, V.M., Devereaux, P.J., Wilczynski, N.L., Morgan, D., Haynes, R.B., Hedges Team, 2004. Doubling the impact: publication of systematic review articles in orthopaedic journals. J Bone Joint Surg Am 86-A (5), 1012e1016, 2004 May. BiHui, J., LiMing, L., Rousseau, R., Egghe, L., 2007. The R- and AR-indices: complementing the h-index. Chin. Sci. Bull. (52), 855e863, 2007. Buehler, L., Rashidi, H.H., 2005. Bioinformatics Basics, Application in Biological Science and Medicine, second ed. CRC Press, Taylor and Francis Group. Callaway, E., 2019. Beat it, impact factor! Publishing elite turns against controversial metric. 2019/10/11 retrieved at. https://www.nature.com/news/polopoly_fs/1. 20224!/menu/main/topColumns/topLeftColumn/pdf/nature.2016.20224.pdf. Chien, T.W., Chang, Y., Wang, H.Y., 2018a. Understanding the productive author who
7
published papers in medicine using National Health Insurance Database: a systematic review and meta-analysis. Medicine (Baltim.) 97 (8), e9967. Chien, T.W., Chow, J.C., Chang, Y., Chou, W., 2018b. Applying Gini coefficient to evaluate the author research domains associated with the ordering of author names: a bibliometric study. Medicine (Baltim.) 97 (39), e12418. Chien, T.W., Wang, H.Y., Chang, Y., Kan, W.C., 2018c. Using Google Maps to display the pattern of coauthor collaborations on the topic of schizophrenia: a systematic review between 1937 and 2017. Schizophr. Res. 204, 206e213. Chien, T.W., Wang, H.Y., Hsu, C.F., Kuo, S.C., 2019. Choropleth map legend design for visualizing the most influential areas in article citation disparities: a bibliometric study. Medicine (Baltim.) 98 (41), e17527. Cromley, R.G., Cromley, E.K., 2009. Choropleth map legend design for visualizing community health disparities. Int. J. Health Geogr. 8, 52. Cromley, R.G., Ye, Y., 2006. Ogive-based legends for choropleth mapping. Cartogr. Geogr. Inf. Sci. 33, 257e268. https://doi.org/10.1559/152304006779500650. deNooy, W., Mrvar, A., Batagelj, V., 2011. Exploratory Social Network Analysis with Pajek: Revised and Expanded, second ed. Cambridge University Press, New York, NY. Egghe, L., 2006. Theory and practise of the g-index. Scientometrics 69, 131e152. Egghe, L., Rousseau, R., Van Hooydonk, G., 2000. Methods for accrediting publications to authors or countries: consequences for evaluation studies. J. Am. Soc. Inf. Sci. 51 (2), 145e157. Fenner, T., Harris, M., Levene, M., Bar-Ilan, J., 2018. A novel bibliometric index with a simple geometric interpretation. PLoS One 13 (7), e0200098. Field Studies Centre, Barcelona, 2019. Choropleth map with proportional symbols. 2019/4/4. available at: https://geographyfieldwork.com/DataPresentation MappingTechniques.htm. Fulginiti, A., Pahwa, R., Frey, L.M., Rice, E., Brekke, J.S., 2016. What factors influence the decision to share suicidal thoughts? A multilevel social network analysis of disclosure among individuals with serious mental illness. Suicide LifeThreatening Behav. 46 (4), 398e412. Han, T.G., Beare, R., Chen, J., Clissold, B., Ly, J., Singhal, S., Ma, H., Srikanth, V., 2017. Googling service boundaries for endovascular clot retrieval hub hospitals in a metropolitan setting: proof-of-concept study. Stroke 48 (5), 1353e1361. Hirsch, J.E., 2005. An index to quantify an individual’s scientific research output. Proc. Natl. Acad. Sci. U.S.A. 102, 16569e16572. Huang, M.H., Chi, P.S., 2010. A comparative analysis of the application of H-index, Gindex, and A-index in institutional-level research evaluation. J. Libr. Inf. Stud. 8 (2), 1e10. JCR, 2019. the5-year impact factor of journal citation reports (JCR). 2018/02/22. available at: http://www.bioxbio.com/if/html/BIOL-PSYCHIAT.html. Journal Editorial, 2016. Time to remodel the journal impact factor. Nature 535, 7613, 466 at. https://www.nature.com/articles/535466a. Kano, N., Seraku, N., Takahashi, F., Tsuji, S., 1984. Attractive quality and must-Be quality. J. Jpn. Soc. Qual. Contr. 41, 39e48. Lindsey, D., 1982. Further evidence for adjusting for multiple authorship. Scientometrics 4 (5), 389e395. Lippi, G., Mattiuzzi, C., 2017. Scientist impact factor (SIF): a new metric for improving scientists’ evaluation? Ann. Transl. Med. 5 (15), 303. Lu, Y., Figler, B., Huang, H., Tu, Y.C., Wang, J., Cheng, F., 2017. Characterization of the mechanism of drug-drug interactions from PubMed using MeSH terms. PLoS One 12 (4), e0173548. Minguet, F., Salgado, T.M., Santopadre, C., Fernandez-Llimos, F., 2017. Redefining the pharmacology and pharmacy subject category in the journal citation reports using medical subject headings (MeSH). Int. J. Clin. Pharm. 39 (5), 989e997. Nielsen, M.B., Seitz, K., 2016. Impact factors and prediction of popular topics in a journal. Ultraschall der Med. 37 (4), 343e345. Pan, R.K., Fortunato, S., 2014. Author Impact Factor: tracking the dynamics of individual scientific impact. Sci. Rep. 4, 4880. Perry, M., Reny, P.J., 2016. How to count citations if you must. Am. Econ. Rev. 106, 2722e2741. Rashidi, H.H., Tran, N.K., Betts, E.V., Howell, L.P., Green, R., 2019. Artificial intelligence and machine learning in pathology: the present landscape of supervised methods. Acad Pathol 6, 2374289519873088. s, M., García-Doval, I., 2018. Rodríguez-Lago, L., Molina-Leyva, A., Pereiro-Ferreiro Influence of article type on the impact factor of dermatology journals. Actas Dermosifiliogr 109 (5), 432e438. Salgado, T.M., Fernandez-Llimos, F., 2019. Missing pharmacy-specific medical subject headings (MeSH) terms: problems and solutions. Res. Soc. Adm. Pharm. 15 (9), 1189e1190. Seglen, P.O., 1997. Why the impact factor of journals should not be used for evaluating research. BMJ 314 (7079), 498e502. Sekercioglu, C.H., 2008. Quantifying coauthor contributions. Science 322, 5900, 371. Thulesius, H., 2011. Assessing research impact with Google Scholar: the most cited articles in the journal 2008-2010. Scand. J. Prim. Health Care 29 (4), 193e195. Tscharntke, T., Hochberg, M.E., Rand, T.A., Resh, V.H., Krauss, J., 2007. Author sequence and credit for contributions in multiauthored publications. PLoS Biol. 5 (1), e18. Vavry cuk, V., 2018. Fair ranking of researchers and research teams. PLoS One 13 (4), e0195509. Waltman, L., 2015. A Review of the Literature on Citation Impact Indicators arXiv, 1507.02099. WHO, 2016. Schizophrenia Fact sheet. 2018/2/23 archived from the original reviewed. April 2016 at. http://www.who.int/mediacentre/factsheets/fs397/en/. Wu, Y., Duan, Z., 2015. Visualization analysis of author collaborations in schizophrenia research. BMC Psychiatry 15, 27. Yang, H., Lee, H.J., 2018. Research trend visualization by MeSH terms from PubMed. Int. J. Environ. Res. Public Health 15 (6) pii: E1113.
Please cite this article as: Lin, C.-H et al., Using the Kano model to display the most cited authors and affiliated countries in schizophrenia research, Schizophrenia Research, https://doi.org/10.1016/j.schres.2019.10.058
Contents lists available at ScienceDirect
Schizophrenia Research journal homepage: www.elsevier.com/locate/schres
Using the Kano model to display the most cited authors and affiliated countries in schizophrenia research Chien-Ho Lin a, Po-Hsin Chou b, c, Willy Chou d, Tsair-Wei Chien e, * a
Department of Psychiatry, Chi Mei Medical Center, Taiwan School of Medicine, National Yang-Ming University, Taipei, Taiwan c Department of Orthopedics and Traumatology, Taipei Veterans General Hospital, Taipei, Taiwan d Department of Physical Medicine and Rehabilitation, Chi Mei Medical Center, Taiwan e Department of Medical Research, Chi Mei Medical Center, Taiwan b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 20 October 2018 Received in revised form 26 October 2019 Accepted 30 October 2019 Available online xxx
In order to improve individual research achievements (IRA), this study investigates which affiliated countries and authors earn the most cited IRAs and whether those types of articles are associated with the number of cited papers on schizophrenia from a leading journal in the field. The Kano model was used for displaying the IRAs. Clusters of medical subject headings (MeSH) were applied to explore the core concepts of a given journal. This study aimed to apply social network analysis (SNA) and an authorship-weighted scheme (AWS) to inspect the association between MeSH terms and IRA. About 2,008 abstracts published between 2012 and 2016 in the journal Schizophrenia Research were downloaded from Pubmed Central using the keyword (Schizophr Res)[Journal] on September 20, 2018. The MeSH terms were clustered by using SNA to separate the core concepts and compare the differences in bibliometric indices (i.e., h, Ag, x and author impact factor or AIF). Visual dashboards were shown on Google Maps. Results indicate that (1) the US, the UK, and Canada earn the highest x-index; (2) the top one author from the US has the highest x-index (¼ 5.73 with x-core at cited ¼ 16.44 and citable ¼ 2); (3) the article type of schizophrenic psychology shows distinctly higher frequencies than others; and (4) article types are associated with the number of cited papers. Four approaches of the Kano model, SNA, MeSH terms, and AWS can be accommodated to display IRAs, classify article types, and quantify coauthor contributions in the article byline, respectively, and applied to other scientific disciplines in the future, not just in this specific journal. © 2019 Elsevier B.V. All rights reserved.
Keywords: Schizophrenia Social network analysis Google maps Pubmed central Authorship-weighted scheme Individual academic achievement
1. Introduction The mental disorder schizophrenia affects over 21 million people worldwide (WHO, 2016; Chien et al., 2018a,b,c), with a lifetime prevalence rate of 1% in the general population (Wu and Duan, 2015). The top 10 journals with the highest numbers of publications on schizophrenia have an Impact Factor of 2 or higher. In this ranking, Schizophrenia Research (SR for short) is at the top, with the most articles published on schizophrenia (329, 35.26%)(Chien et al., 2018a,b,c), and a journal impact factor (JIF) of 3.986 in 2017 (JCR, 2019). In relation to these, the authors (or the article types) who
* Corresponding author. Taipei Veterans General Hospital, 18F, 201, Section 2, Shipai Road, Beitou District, Taipei,112, Taiwan. E-mail addresses: [email protected] (C.-H. Lin), [email protected] (P.-H. Chou), [email protected] (W. Chou), [email protected] (T.-W. Chien).
contributed most to the JIF for SR and the article types associated with the number of cited papers in SR remain unknown. Many papers (Chien et al., 2018a,b,c; Fulginiti et al., 2016; Wu and Duan, 2015) investigated author collaborations in schizophrenia using social network analysis (SNA). Medical Subject Headings (MeSH) is a useful way for classifying the scope(s) of a given journal(Minguet et al., 2017) (or, core concepts in this study), similar to the unsupervised training procedure in machine learning(Buehler and Rashidi, 2005; Rashidi, 2019), by differentiating the patterns of article MeSH terms. As such, the connections among these MeSH terms are worthy of using SNA(Chien et al., 2018a,b,c; Chien et al., 2018; Lu et al., 2017) based on the co-occurrence phenomena. However, to date, no other matching scheme has been applied to scientific disciplines for determining which article types contribute the most in improving the JIF for a given journal. The prediction of article types related to the number of citations can be verified if the article types (e.g., using MeSH terms) have been
https://doi.org/10.1016/j.schres.2019.10.058 0920-9964/© 2019 Elsevier B.V. All rights reserved.
Please cite this article as: Lin, C.-H et al., Using the Kano model to display the most cited authors and affiliated countries in schizophrenia research, Schizophrenia Research, https://doi.org/10.1016/j.schres.2019.10.058
2
C.-H. Lin et al. / Schizophrenia Research xxx (xxxx) xxx
properly classified. Every June, millions of academic scholars pay close attention to the Journal Citation Reports (JCR) ranking of the JIF for each indexed journal. However, no author IFs (AIFs) (Lippi and Mattiuzzi, 2017; Pan and Fortunato, 2014) or bibliometric indices (Hirsch, 2005; Egghe, 2006; Fenner et al., 2018) have gained scientists’ or scholars’ attention as much as JIF does. The reasons for this might be the lack of a simple 5-year moving average method(Pan, 2014) that can be along with an established authorship-weighted scheme (AWS)(Chien et al., 2018a,b,c; Sekercioglu, 2008; Vavry cuk, 2018) for tracking the dynamics of individual scientific impact and quantifying the coauthor contributions. Many counting schemes for quantifying coauthor contributions(Sekercioglu, 2008) (e.g., fractional counting (Batista et al., 2006; Egghe et al., 2000; Lindsey, 1982; Tscharntke et al., 2007; Waltman, 2015) and authorship-weighted counting(Egghe et al., 2000; Tscharntke et al., 2007; Waltman, 2015) have been proposed as an alternative to the traditional full counting (i.e., all authors contributed equally to an article). Thus far, we have not seen any articles completely applying the AWS to any academic discipline in the past. In the current study, we attempt to (1) quantify coauthor contributions with AWS to present the most cited affiliated countries and authors using a Kano diagram (Kano et al., 1984) to display, and (2) inspect the association between MeSH terms and the individual research achievements (IRA).
The sum of author weights in a byline ¼
m1 X k¼0
expðgj Þ Pm1 j ¼ 0 expðgj Þ (3)
The sum of authorships equals 1 for each paper referred to Eq. (3). This is a basic concept that ensures that all papers have an equal weight irrespective of the number of co-authors(Vavry cuk, 2018). Accordingly, more importance is given to the first (¼ exp(m-1), primary) and the last (¼ exp(m-2), corresponding or supervisory) authors, and it is assumed that the others (the middle authors) have made smaller contributions (Egghe et al., 2000). In Eq. (3), the smallest portion (¼ exp(0) ¼ 1) is assigned to the last second author with the odds ¼ 1 as the basic reference. 2.2.2. Using x-index to display IRA on maps All authors’ citation numbers for each article are displayed in descending order along with the ascending sequential integral number(i) from 1 to n(¼the number of publications). The x-indexes qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi [ ¼ maxði ci Þ, where all the number of cited papers are denoted i
by ci] based on the x-core publications at i(Fenner et al., 2018), were applied to obtain the research achievements for affiliated counties and authors, respectively. The choropleth maps (Chien et al., 2019; Barcelona Field Studies Centre, 2019; Cromley and Ye, 2006; Cromley, Cromley, 2009) and the Kano diagram (Kano et al., 1984) were used for displaying the IRAs, see Additional File 3.
2. Methods 2.3. Objective 2: Inspecting the association between MeSH terms and the IRA
2.1. Data source We searched the database in the Pubmed Center (PMC) using the keyword (Schizophr Res)[Journal] on September 20, 2018. A total of 2,008 abstracts published between 2012 and 2016 were identified. We created a Microsoft Excel VBA (visual basic for application) module to handle the data, see Additional Files 1 and 2. All downloaded abstracts met the requirement for the type of journal article. Ethical approval was not necessary for this study, as no human subjects or personal data were accessed. 2.2. Objective 1: Using AWS to present the most cited entities on a Kano diagram 2.2.1. The author-based perspective The AWS and the AIF calculations are shown in Equations (1) and (2) (Chien et al., 2018a,b,c):
expðgj Þ 2:72gj ¼ Pm1 Wj ¼ Pm1 ; gj j ¼ 0 expðgj Þ j ¼ 0 2:72
P AIF ¼
(1)
2.3.1. Using social network analysis to cluster MeSH terms Social network analysis (SNA) (Chien et al., 2018a,b,c; Chien et al., 2018; Lu et al., 2017) helps explore the pattern of entities in a network. Pajek (Batagelj and Mrvar, 2003; deNooy et al., 2011; Minguet et al., 2017) is a widely used SNA program(Chien et al., 2018a,b,c). In keeping with the Pajek guidelines, we defined the MESH term as a node connecting to other nodes through the edge. Usually, the weight between the two nodes is determined by the number of connections. Centrality is a vital index to analyze the network. Any individual or keyword that lies in the center of the social network can determine its influence on the system and its ability to gain information(Han, 2017). We applied betweenness and degree centrality for selecting the most influential MeSH terms. The algorithm of community partition was applied to identify and separate clusters. 2.3.2. Citations on MeSH terms Similar to the IRA for authors, all weights for each MeSH term in an article are equal (i.e., the power gj equals zero for each MeSH
Cited:papers:based:on: Wj :in:a:given:year:and:the:proceeding:5:yrs Citable:papers: Wj :in:the:given:5:yrs
Considering a paper of mþ1 authors with the last being the corresponding author, Wj in Eq. (1) denotes the weight for an author on the order j in the article byline. The power gj is an integer number from m-1 to 0 in descending order.
(2)
term in Eq. (1) or Wj ¼ 1/n, n denotes for the number of MeSH terms in the article). The bibliometric indices (i.e., h, g, x (Hirsch, 2005; Egghe, 2006; Fenner et al., 2018) and AIF (Lippi and Mattiuzzi, 2017; Pan and Fortunato, 2014) on each MeSH term were computed according to their core citations and publications. The highest MeSH term in their own cluster stands for the article type.
Please cite this article as: Lin, C.-H et al., Using the Kano model to display the most cited authors and affiliated countries in schizophrenia research, Schizophrenia Research, https://doi.org/10.1016/j.schres.2019.10.058
C.-H. Lin et al. / Schizophrenia Research xxx (xxxx) xxx
3
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pg h(ci ), g( i¼1 ci /g), x-indexes[ ¼ maxði ci Þ, AIF in Eq. (2), and i
the Ag defined by accumulative citations and the number of core Pg articles at g as c =g to improve the IRA discrimination in i¼1 i ranking comparison (Huang and Chi, 2010). The study flowchart is shown in Fig. 1. 3. Results 3.1. The most cited affiliated countries The top three most cited affiliated countries are the US, the UK, and Canada; see Fig. 2. The most publication outputs in SR from 2012 to 2016 came from the U.S. (686, 34.16%). The UK and Brazil present an increasing trend (0.72 and 0.63, respectively), as shown in the last column in Table 1. The US has earned the highest x-index (35.95) over the past five years in SR. Relatively, SR has gained an xindex with 54.44 points. The Kano diagram separates all countries into three parts (i.e., the citation- originated excitement, the one-dimension performance, and the basic publication-originated requirement). Only the US is classified as the publication-originated requirement at the bottom right in Fig. 3. 3.2. The most cited authors Fig. 1. Study flowchart.
2.3.3. Comparison of article types in metrics Four bibliometric indices for the representative MeSH term for the top 9 clusters were computed using the following formulas:
The author Gregory P. Strauss from the US has the highest xindex (¼ 5.73 with x-core at cited ¼ 16.44 and citable ¼ 2); see Fig. 4. The author Jason Schiffman from the US published seven articles in SR from 2012 to 2016 with x-index ¼ 2.36, Cited ¼ 0.7, and Citable ¼ 8; see the right-bottom corner in Fig. 4. Interested readers are encouraged to scan the QR code on the Figure to see more information about the authors on the Kano diagram.
Fig. 2. The most cited countries around the world in SR.
Please cite this article as: Lin, C.-H et al., Using the Kano model to display the most cited authors and affiliated countries in schizophrenia research, Schizophrenia Research, https://doi.org/10.1016/j.schres.2019.10.058
4
C.-H. Lin et al. / Schizophrenia Research xxx (xxxx) xxx
Table 1 Distribution of publication outputs across continents and over the years. Continent
2012
2013
2014
2015
2016
Total
%
Growth
AFRICA South Africa Ethiopia Others ASIA China Japan Taiwan Israel Hong Kong Others EUROPE U.K. Germany Spain Netherlands France Others N. AMERICA U.S. Canada Mexico Cuba OCEANIA Australia New Zealand Aruba S. AMERICA Brazil Argentina Others Total
2 1 1
4 2
4 3 1
3 1 1 1 67 17 12 9 4 4 21 148 24 22 18 9 16 59 202 163 34 4 1 26 25 1
1 1
54 12 18 5 3 5 11 135 36 17 20 12 8 42 130 104 25 1
26 20 3 3 472
13 10 2 1 345
14 8 3 3 313 77 74 36 29 28 69 687 137 91 87 82 56 234 833 686 135 10 2 95 90 4 1 66 54 6 6 2008
0.70 0.40 0.15 0.15 15.59 3.83 3.69 1.79 1.44 1.39 3.44 34.21 6.82 4.53 4.33 4.08 2.79 11.65 41.48 34.16 6.72 0.50 0.10 4.73 4.48 0.20 0.05 3.29 2.69 0.30 0.30 100.00
0.36 0.18 0.29 0.18 0.17 0.18 0.44 0.46 0.94 0.69 0.44 0.49 0.72 0.28 0.20 0.65 0.36 0.43 0.14 0.30 0.49 0.40 0.29 0.09 0.03 0.71 0.00 0.65 0.63 0.61 0.61 0.16
49 12 8 8 10 6 5 115 22 18 13 18 5 39 153 130 23
2 65 8 19 9 6 7 16 146 22 16 25 29 11 43 181 156 23 2
17 16 1
19 18 1
6 5 1
7 7
78 28 17 5 6 6 16 143 33 18 11 14 16 51 167 133 30 3 1 21 19 1 1 14 12
342
422
2 427
12 12
x-index
3.08
11.4 8.63 6.56 5.79
14.57 11.5 9.94 11.79 7.29
35.95 13.11 3.24 1.12 10.67 1.41 7.11 2.75 54.44
Fig. 3. Using the x-index to display the most cited countries on the Kano diagram.
3.3. Clusters of MeSH terms The top nine MeSH clusters are presented in Fig. 5, and the representative terms with the most influential betweenness centrality are shown for each cluster. The bigger one is that of “schizophrenic psychology.” Interested readers are encouraged to scan the QR code on Fig. 5 to see more details in the PMC by clicking “publication” on the map when the specific MeSH bubble is selected.
Fig. 4. Using the x-index to display the most cited authors on the Kano diagram.
higher metric (averaged by Ag, x, and h) than others. 4. Discussion 4.1. Principal findings
3.4. Comparison of article types in metrics One-way ANOVA was performed to present the article types that are statistically distinct [F(20,8) ¼ 13.46, p < 0.001] in SR (Fig. 6). We can see that the core concept of “schizophrenic psychology” has a
Results indicate that (1) the US, the UK, and Canada earn the highest x-index; (2) author Gregory P. Strauss from the US has the highest x-index (¼ 5.73 with x-core at cited ¼ 16.44 and citable ¼ 2); (3) the article type of schizophrenic psychology shows
Please cite this article as: Lin, C.-H et al., Using the Kano model to display the most cited authors and affiliated countries in schizophrenia research, Schizophrenia Research, https://doi.org/10.1016/j.schres.2019.10.058
C.-H. Lin et al. / Schizophrenia Research xxx (xxxx) xxx
5
Fig. 5. Cluster analysis of MeSH terms using betweenness centrality for Schizophr Res.
2011), all of which are classified by subjective human judgments. We applied SNA to classify MeSH terms and then assigned articles into an appropriate cluster, through which the bibliometric metrics for each article type can be computed by the respective number of article citations and publications. To the best of our knowledge, no paper has adopted this objective way of analyzing the relationship between MeSH terms and citation metrics as we did in this study, though MeSH terms for characterizing journal features have been applied to articles(Balogh et al., 2019; Chien et al., 2018a,b,c; Salgado and Fernandez-Llimos, 2019; Yang and Lee, 2018).
Fig. 6. Comparison of bibliometric indices among clusters.
distinctly higher frequencies than others; and (4) article types are associated with the number of cited papers. The concept of “schizophrenic psychology” has significantly higher bibliometric metrics compared to other counterparts [F(20,8) ¼ 23.36, p < 0.001]; see Fig. 6. There are three features in this study, as indicated in the following: (1) classifying the type of articles (or core concept) using MeSH terms, (2) quantifying the coauthor contributions with AWS, and (3) demonstrating the affiliated countries and authors that have the highest research achievements in SR as shown on Google Maps. 4.2. Features and implications in this study 4.2.1. Classifying article types Traditionally, articles are classified based on the types of (i) case reports, reviews, and original articles (Bhandari et al., 2004; Rodríguez-Lago et al., 2018), (ii) systematic reviews and other systematic or narrative reviews (Alotaibi et al., 2016; Thulesius, 2011), and (iii) methodology and design or others(Thulesius,
4.2.2. Quantifying coauthor contributions with AWS Although the h-index(Hirsch, 2005) and others (Egghe, 2006; Fenner et al., 2018; Lippi and Mattiuzzi, 2017; Pan and Fortunato, 2014) have been suggested for measuring IRA in scientific disciplines, one of their shortcomings is the assumption of equal credits for all co-authors in an article(Chien et al., 2018a,b,c; Sekercioglu, 2008; Vavry cuk, 2018). Many formulas have been proposed in the literature((Batista et al., 2006; Egghe et al., 2000; Lindsey, 1982; Tscharntke et al., 2007; Waltman, 2015; Egghe et al., 2000; Tscharntke et al., 2007; Waltman, 2015). As of publication, we have yet to encounter an empirical study that can solve the problem of quantifying co-author contributions(Sekercioglu, 2008) in academics. 4.2.3. Entities on the Kano diagram using Google Maps to display We show our resulting dashboard on Google Maps, which allows readers to examine the details of the information by manipulating the zoom-in and zoom-out functionalities. This is a better representation than the traditional ones, which used static images in an article. In particular, both the choropleth map and the Kano diagram are present in Figs. 2e4, which make it easy for readers to visualize representations and to catch the highlight results of interest. 4.2.4. MeSH terms used for representing article types Compared to previous studies (Bhandari et al., 2004; Nielsen
Please cite this article as: Lin, C.-H et al., Using the Kano model to display the most cited authors and affiliated countries in schizophrenia research, Schizophrenia Research, https://doi.org/10.1016/j.schres.2019.10.058
6
C.-H. Lin et al. / Schizophrenia Research xxx (xxxx) xxx
and Seitz, 2016; Rodríguez-Lago et al., 2018), this study reports (1) a higher impact factor being associated with the publication of reviews and original articles instead of those case reports, (2) rigorous systematic reviews receiving more citations than other narrative reviews, and (3) case reports with low IFs due to their infrequent citation by articles. The MeSH clusters is a new approach and is markedly different from previous studies in that it can objectively verify the core concepts of an article for a given journal. As such, the bibliometric metrics can be obtained if each article has been assigned to the corresponding type. 4.2.5. The reason for using x-index as an IRA metric The reason we prefer the x-index to the h-index for demonstration purposes in Figs. 2e4 is explained by the following three extreme examples(Fenner et al., 2018): if one author has a single publication with 100 citations and another has ten publications each with ten citations, then the h-index of the former is one, while the hindex of the latter is 10. At the other extreme, an author with 100 publications, each with a single citation, has an h-index of 1. If the xindex is applied, all those examples mentioned above would reach an identical value of 10 for measuring the IRA. Alternatively, the gindexes are similar to the h-indexes at 1, 10, and 1. The Ag-indexes are located at 100, 10, and 1. Hence, the x-index can reasonably be used to discriminate IRA on axis Y in Fig. 5 and Ag on axis X. In addition, the x-index is suitable for using the Kano diagram to examine whether the entities are citation- or publication-oriented requirements in attributes. There are many other bibliometric indices, such as the R-index qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffi Ph P (¼ Ah, where A ¼ hi¼1 ci =h)(BiHui et al., 2007), and i¼1 ci ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Ph 2 the Euclidean-index(¼ i¼1 ci )(Perry and Reny, 2016), each with their own sets of features and limitations. All of those indices would have high correlations with one another in comparison to the AIF. The reasons for JCR preferring JIF to metrics can be seen from the results of this study, which reveal that (1) metrics are easily inflated by the number of publications, and (2) the JIF penalizes the journal with a high quantity of outputs. It is often easier for journals than for individuals to manipulate publication output. 4.2.6. Few studies using PMC citations to explore IRAs Another notable feature is the PMC citations used in this study. Over 100 papers were found by searching for the “most-cited articles” [Title] in the Pubmed library on October 10, 2018. Most of them have applied academic databases to conduct the citation analysis, such as the Scientific Citation Index (SCI; Thomson Reuters, New York, NY, the United States), Scopus (Elsevier, Amsterdam, the Netherlands), and Google Scholar (Alotaibi et al., 2016; Thulesius, 2011), to investigate the most-cited articles in a specific discipline. Only two(Chien et al., 2018/2019) download citation records from PubMed library (i.e., a free search engine accessing the PMC database of references and abstracts of papers on life sciences and biomedical topics). Using Pubmed citations to download citations and analyze the association between article types and the number of cited papers is worthy of further investigation in the future.
same name or use the same abbreviations but are affiliated with different institutions. Third, using MeSH terms to define the article type is arbitrary. The concept should be inducted from all, or at least two or three, main elements instead of the principal one. For example, schizophrenic psychology is related to complications and diagnosis; drug therapy is close to therapeutic use and drug effects, and metabolism is associated with pathology and enzymology. Interested readers are encouraged to scan the QR-code on Fig. 2 to examine more relevant MeSH terms in a cluster to define the true concept for the latent cluster. Fourth, although our cluster analysis and the AWS formula are useful approaches for verifying the association of MeSH terms and the number of weighted cited papers for individual authors, the results may be affected by the accuracy of MeSH terms and the authors’ actual contributions instead of the last as the true corresponding author. We used a variety of methods for cleaning and identifying data in this research, but some errors still exist, and these may affect the cluster results to some extent. Fifth, the MeSH terms clustered by the social network analysis are merely defined by the representative with the highest centrality degree in the respective cluster, which is arbitrary enough to bias the resulting and the inference making. Although the unsupervised training procedure in machine learning (Buehler and Rashidi, 2005; Rashidi, 2019) has been applied to differentiate the patterns of article MeSH terms using SNA in this study, the article types should be defiend more cautious not only in this study but also in the future. Finally, some terms in Fig. 5 are unfamiliar to psychiatrists in clinical settings, like schizophrenic psychology and physiopathology which were classified and named by the MeSH team in PMC. The term of schizophrenic psychology should be psychopathology or symptomatology in terminologies. Similarly, the term of psysiopathology should be pathophysiology that is used in scientific communication in psychiatry and psychology diagnostic classification. Furthermore, many researchers believe that the impact factor or citation does not reflect the scientific quality of the research work(Callaway, 2019; Journal Editorial, 2016; Seglen, 1997). Many journals are now preferring citation distribution curve over JIF(Callaway, 2019). Authors can discuss how this method for author and journal analyses can be included. Whether the x-index(Fenner et al., 2018) that is applied in this study can be used for journal research achievements is worthy of further studies and discussions in the future. 5. Conclusions By the above results and discussion, some valuable results are obtained, including the article types of a scholarly journal associated with the number of the cited metrics. Results suggest that both the methods of classifying article types and quantifying coauthor contributions might be applied to other scientific disciplines. As such, which type of articles and who contributes most to a specific journal can be evaluated by scientists and scholars in the future.
4.3. Limitations
Ethics approval and consent to participate
Although the findings are based on the above analysis, there are still several potential limitations that may encourage further research efforts. First, this study only focuses on one journal, so it cannot be generalized to other fields, particularly those with different characteristics and science categories. Second, there might be some biases in author identification because some authors in the bibliometric database may have the
Not applicable. All data were downloaded from the MEDLINE database at pubmed.com. Funding There are no sources of funding to be declared.
Please cite this article as: Lin, C.-H et al., Using the Kano model to display the most cited authors and affiliated countries in schizophrenia research, Schizophrenia Research, https://doi.org/10.1016/j.schres.2019.10.058
C.-H. Lin et al. / Schizophrenia Research xxx (xxxx) xxx
Authors’ contributions CH conceived and designed the study, PH and WC interpreted the data, and TW monitored the process. CH drafted the manuscript. All authors read the paper and approved the final manuscript. Authors’ information CH and PH are MD and assistant professor, Both WC and TW are associated professors at ChiMei Medical Center, Taiwan. Consent to publish Not applicable. Availability of data and materials All data used in this study are available in Additional files. Declaration of competing interest The authors declare that they have no competing interests. Acknowledgments We thank Enago (www.enago.tw) for the English language review of this manuscript and the fundng source from Chi Mei Medical Center (Taiwan) in 2017 (ID¼CMFHR10627). All authors declare no conflicts of interest. List of abbreviations AIF AWS IRA JIF MeSH PMC SNA SR
author impact factor authorship-weighted scheme individual research achievements journal impact factor medical subject headings Pubmed Central social network analysis Schizophrenia Research
Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.schres.2019.10.058. References Alotaibi, N.M., Nassiri, F., Badhiwala, J.H., Witiw, C.D., Ibrahim, G.M., Macdonald, R.,L., Lozano, A.M., 2016. The most cited works in aneurysmal subarachnoid hemorrhage: a bibliometric analysis of the 100 most cited art. World Neurosurg 89, 587e592 e6. Balogh, S.G., Zagyva, D., Pollner, P., Palla, G., 2019. Time evolution of the hierarchical networks between PubMed MeSH terms. PLoS One 14 (8), e0220648. Batagelj, V., Mrvar, A., 2003. Pajek - analysis, and visualization of large networks. In: inJünger, M., Mutzel, P. (Eds.), Graph Drawing Software. Springer, Berlin, pp. 77e103. Batista, P.D., Campiteli, M.G., Kinouchi, O., 2006. Is it possible to compare researchers with different scientific interests? Scientometrics 68 (1), 179e189. Bhandari, M., Montori, V.M., Devereaux, P.J., Wilczynski, N.L., Morgan, D., Haynes, R.B., Hedges Team, 2004. Doubling the impact: publication of systematic review articles in orthopaedic journals. J Bone Joint Surg Am 86-A (5), 1012e1016, 2004 May. BiHui, J., LiMing, L., Rousseau, R., Egghe, L., 2007. The R- and AR-indices: complementing the h-index. Chin. Sci. Bull. (52), 855e863, 2007. Buehler, L., Rashidi, H.H., 2005. Bioinformatics Basics, Application in Biological Science and Medicine, second ed. CRC Press, Taylor and Francis Group. Callaway, E., 2019. Beat it, impact factor! Publishing elite turns against controversial metric. 2019/10/11 retrieved at. https://www.nature.com/news/polopoly_fs/1. 20224!/menu/main/topColumns/topLeftColumn/pdf/nature.2016.20224.pdf. Chien, T.W., Chang, Y., Wang, H.Y., 2018a. Understanding the productive author who
7
published papers in medicine using National Health Insurance Database: a systematic review and meta-analysis. Medicine (Baltim.) 97 (8), e9967. Chien, T.W., Chow, J.C., Chang, Y., Chou, W., 2018b. Applying Gini coefficient to evaluate the author research domains associated with the ordering of author names: a bibliometric study. Medicine (Baltim.) 97 (39), e12418. Chien, T.W., Wang, H.Y., Chang, Y., Kan, W.C., 2018c. Using Google Maps to display the pattern of coauthor collaborations on the topic of schizophrenia: a systematic review between 1937 and 2017. Schizophr. Res. 204, 206e213. Chien, T.W., Wang, H.Y., Hsu, C.F., Kuo, S.C., 2019. Choropleth map legend design for visualizing the most influential areas in article citation disparities: a bibliometric study. Medicine (Baltim.) 98 (41), e17527. Cromley, R.G., Cromley, E.K., 2009. Choropleth map legend design for visualizing community health disparities. Int. J. Health Geogr. 8, 52. Cromley, R.G., Ye, Y., 2006. Ogive-based legends for choropleth mapping. Cartogr. Geogr. Inf. Sci. 33, 257e268. https://doi.org/10.1559/152304006779500650. deNooy, W., Mrvar, A., Batagelj, V., 2011. Exploratory Social Network Analysis with Pajek: Revised and Expanded, second ed. Cambridge University Press, New York, NY. Egghe, L., 2006. Theory and practise of the g-index. Scientometrics 69, 131e152. Egghe, L., Rousseau, R., Van Hooydonk, G., 2000. Methods for accrediting publications to authors or countries: consequences for evaluation studies. J. Am. Soc. Inf. Sci. 51 (2), 145e157. Fenner, T., Harris, M., Levene, M., Bar-Ilan, J., 2018. A novel bibliometric index with a simple geometric interpretation. PLoS One 13 (7), e0200098. Field Studies Centre, Barcelona, 2019. Choropleth map with proportional symbols. 2019/4/4. available at: https://geographyfieldwork.com/DataPresentation MappingTechniques.htm. Fulginiti, A., Pahwa, R., Frey, L.M., Rice, E., Brekke, J.S., 2016. What factors influence the decision to share suicidal thoughts? A multilevel social network analysis of disclosure among individuals with serious mental illness. Suicide LifeThreatening Behav. 46 (4), 398e412. Han, T.G., Beare, R., Chen, J., Clissold, B., Ly, J., Singhal, S., Ma, H., Srikanth, V., 2017. Googling service boundaries for endovascular clot retrieval hub hospitals in a metropolitan setting: proof-of-concept study. Stroke 48 (5), 1353e1361. Hirsch, J.E., 2005. An index to quantify an individual’s scientific research output. Proc. Natl. Acad. Sci. U.S.A. 102, 16569e16572. Huang, M.H., Chi, P.S., 2010. A comparative analysis of the application of H-index, Gindex, and A-index in institutional-level research evaluation. J. Libr. Inf. Stud. 8 (2), 1e10. JCR, 2019. the5-year impact factor of journal citation reports (JCR). 2018/02/22. available at: http://www.bioxbio.com/if/html/BIOL-PSYCHIAT.html. Journal Editorial, 2016. Time to remodel the journal impact factor. Nature 535, 7613, 466 at. https://www.nature.com/articles/535466a. Kano, N., Seraku, N., Takahashi, F., Tsuji, S., 1984. Attractive quality and must-Be quality. J. Jpn. Soc. Qual. Contr. 41, 39e48. Lindsey, D., 1982. Further evidence for adjusting for multiple authorship. Scientometrics 4 (5), 389e395. Lippi, G., Mattiuzzi, C., 2017. Scientist impact factor (SIF): a new metric for improving scientists’ evaluation? Ann. Transl. Med. 5 (15), 303. Lu, Y., Figler, B., Huang, H., Tu, Y.C., Wang, J., Cheng, F., 2017. Characterization of the mechanism of drug-drug interactions from PubMed using MeSH terms. PLoS One 12 (4), e0173548. Minguet, F., Salgado, T.M., Santopadre, C., Fernandez-Llimos, F., 2017. Redefining the pharmacology and pharmacy subject category in the journal citation reports using medical subject headings (MeSH). Int. J. Clin. Pharm. 39 (5), 989e997. Nielsen, M.B., Seitz, K., 2016. Impact factors and prediction of popular topics in a journal. Ultraschall der Med. 37 (4), 343e345. Pan, R.K., Fortunato, S., 2014. Author Impact Factor: tracking the dynamics of individual scientific impact. Sci. Rep. 4, 4880. Perry, M., Reny, P.J., 2016. How to count citations if you must. Am. Econ. Rev. 106, 2722e2741. Rashidi, H.H., Tran, N.K., Betts, E.V., Howell, L.P., Green, R., 2019. Artificial intelligence and machine learning in pathology: the present landscape of supervised methods. Acad Pathol 6, 2374289519873088. s, M., García-Doval, I., 2018. Rodríguez-Lago, L., Molina-Leyva, A., Pereiro-Ferreiro Influence of article type on the impact factor of dermatology journals. Actas Dermosifiliogr 109 (5), 432e438. Salgado, T.M., Fernandez-Llimos, F., 2019. Missing pharmacy-specific medical subject headings (MeSH) terms: problems and solutions. Res. Soc. Adm. Pharm. 15 (9), 1189e1190. Seglen, P.O., 1997. Why the impact factor of journals should not be used for evaluating research. BMJ 314 (7079), 498e502. Sekercioglu, C.H., 2008. Quantifying coauthor contributions. Science 322, 5900, 371. Thulesius, H., 2011. Assessing research impact with Google Scholar: the most cited articles in the journal 2008-2010. Scand. J. Prim. Health Care 29 (4), 193e195. Tscharntke, T., Hochberg, M.E., Rand, T.A., Resh, V.H., Krauss, J., 2007. Author sequence and credit for contributions in multiauthored publications. PLoS Biol. 5 (1), e18. Vavry cuk, V., 2018. Fair ranking of researchers and research teams. PLoS One 13 (4), e0195509. Waltman, L., 2015. A Review of the Literature on Citation Impact Indicators arXiv, 1507.02099. WHO, 2016. Schizophrenia Fact sheet. 2018/2/23 archived from the original reviewed. April 2016 at. http://www.who.int/mediacentre/factsheets/fs397/en/. Wu, Y., Duan, Z., 2015. Visualization analysis of author collaborations in schizophrenia research. BMC Psychiatry 15, 27. Yang, H., Lee, H.J., 2018. Research trend visualization by MeSH terms from PubMed. Int. J. Environ. Res. Public Health 15 (6) pii: E1113.
Please cite this article as: Lin, C.-H et al., Using the Kano model to display the most cited authors and affiliated countries in schizophrenia research, Schizophrenia Research, https://doi.org/10.1016/j.schres.2019.10.058