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.

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.

Comparison of Ferguson's δ and the Gini coefficient used for measuring the inequality of data related to health quality of life outcomes.

BACKGROUND: Ferguson's δ and Gini coefficient (GC) are defined as contrasting statistical measures of inequality among members within populations. However, the association and cutting points for these two statistics are still unclear; a visual display is required to inspect their similarities and differences. METHODS: A simulation study was conducted to illustrate the pertinent properties of these statistics, along with Cronbach's α and dimension coefficient (DC) to assess inequality. We manipulated datasets containing four item lengths with two number combinations (0 and 33%) in item length if two domains exist. Each item difficulty with five-point polytomous responses was uniformly distributed across a ± 2 logit range. A simulated response questionnaire was designed along with known different structures of true person scores under Rasch model conditions. This was done for 20 normally distributed sample sizes. A total of 320 scenarios were administered. Four coefficients (Ferguson's δ, GC, test reliability Cronbach's α, and DC) were simultaneously calculated for each simulation dataset. Box plots were drawn to examine which of these presented the correct property of inequality on data. Two examples were illustrated to present the index on Google Maps for securing the discriminatory power of individuals. RESULTS: We found that 1-Ferguson's δ coefficient has a high correlation (0.95) with GC. The cutting points of Ferguson's δ, GC, test reliability Cronbach's α, and the DC are 0.15, 0.50, 0.70, and 0.67, respectively. Two applications are shown on Google Maps with GCs of 0.14 and 0.42, respectively. Histogram legends and Lorenz curves are used to display the results. CONCLUSION: The GC is recommended to readers as an index for measuring the extent of inequality (or lower discrimination power) in a given dataset. It can also show the study results of person measures to determine the inequality in the health-related quality of life outcomes.

Detecting hospital behaviors of up-coding on DRGs using Rasch model of continuous variables and online cloud computing in Taiwan.

BACKGROUND: This work aims to apply data-detection algorithms to predict the possible deductions of reimbursement from Taiwan's Bureau of National Health Insurance (BNHI), and to design an online dashboard to send alerts and reminders to physicians after completing their patient discharge summaries. METHODS: Reimbursement data for discharged patients were extracted from a Taiwan medical center in 2016. Using the Rasch model of continuous variables, we applied standardized residual analyses to 20 sets of norm-referenced diagnosis-related group (DRGs), each with 300 cases, and compared these to 194 cases with deducted records from the BNHI. We then examine whether the results of prediction using the Rasch model have a high probability associated with the deducted cases. Furthermore, an online dashboard was designed for use in the online monitoring of possible deductions on fee items in medical settings. RESULTS: The results show that 1) the effects deducted by the NHRI can be predicted with an accuracy rate of 0.82 using the standardized residual approach of the Rasch model; 2) the accuracies for drug, medical material and examination fees are not associated among different years, and all of those areas under the ROC curve (AUC) are significantly greater than the randomized probability of 0.50; and 3) the online dashboard showing the possible deductions on fee items can be used by hospitals in the future. CONCLUSION: The DRG-based comparisons in the possible deductions on medical fees, along with the algorithm based on Rasch modeling, can be a complementary tool in upgrading the efficiency and accuracy in processing medical fee applications in the discernable future.

Application of a multidimensional computerized adaptive test for a Clinical Dementia Rating Scale through computer-aided techniques.

BACKGROUND: With the increasingly rapid growth of the elderly population, individuals aged 65 years and above now compose 14% of Taiwanese citizens, thereby making Taiwanese society an aged society. A leading factor that affects the elderly population is dementia. A method of precisely and efficiently examining patients with dementia through multidimensional computer adaptive testing (MCAT) to accurately determine the patients' stage of dementia needs to be developed. This study aimed to develop online MCAT that family members can use on their own computers, tablets, or smartphones to predict the extent of dementia for patients responding to the Clinical Dementia Rating (CDR) instrument. METHODS: The CDR was applied to 366 outpatients in a hospital in Taiwan. MCAT was employed with parameters for items across eight dimensions, and responses were simulated to compare the efficiency and precision between MCAT and non-adaptive testing (NAT). The number of items saved and the estimated person measures was compared between the results of MCAT and NAT, respectively. RESULTS: MCAT yielded substantially more precise measurements and was considerably more efficient than NAT. MCAT achieved 20.19% (= [53 - 42.3]/53) saving in item length when the measurement differences were less than 5%. Pearson correlation coefficients were highly consistent among the eight domains. The cut-off points for the overall measures were - 1.4, - 0.4, 0.4, and 1.4 logits, which was equivalent to 20% for each portion in percentile scores. Substantially fewer items were answered through MCAT than through NAT without compromising the precision of MCAT. CONCLUSIONS: Developing a website that family members can use on their own computers, tablets, and smartphones to help them perform online screening and prediction of dementia in older adults is useful and manageable.

Development of a Microsoft Excel tool for one-parameter Rasch model of continuous items: an application to a safety attitude survey.

BACKGROUND: Many continuous item responses (CIRs) are encountered in healthcare settings, but no one uses item response theory's (IRT) probabilistic modeling to present graphical presentations for interpreting CIR results. A computer module that is programmed to deal with CIRs is required. To present a computer module, validate it, and verify its usefulness in dealing with CIR data, and then to apply the model to real healthcare data in order to show how the CIR that can be applied to healthcare settings with an example regarding a safety attitude survey. METHODS: Using Microsoft Excel VBA (Visual Basic for Applications), we designed a computer module that minimizes the residuals and calculates model's expected scores according to person responses across items. Rasch models based on a Wright map and on KIDMAP were demonstrated to interpret results of the safety attitude survey. RESULTS: The author-made CIR module yielded OUTFIT mean square (MNSQ) and person measures equivalent to those yielded by professional Rasch Winsteps software. The probabilistic modeling of the CIR module provides messages that are much more valuable to users and show the CIR advantage over classic test theory. CONCLUSIONS: Because of advances in computer technology, healthcare users who are familiar to MS Excel can easily apply the study CIR module to deal with continuous variables to benefit comparisons of data with a logistic distribution and model fit statistics.

Development of a Microsoft Excel tool for applying a factor retention criterion of a dimension coefficient to a survey on patient safety culture.

BACKGROUND: Many quality-of-life studies have been conducted in healthcare settings, but few have used Microsoft Excel to incorporate Cronbach's α with dimension coefficient (DC) for describing a scale's characteristics. To present a computer module that can report a scale's validity, we manipulated datasets to verify a DC that can be used as a factor retention criterion for demonstrating its usefulness in a patient safety culture survey (PSC). METHODS: Microsoft Excel Visual Basic for Applications was used to design a computer module for simulating 2000 datasets fitting the Rasch rating scale model. The datasets consisted of (i) five dual correlation coefficients (correl. = 0.3, 0.5, 0.7, 0.9, and 1.0) on two latent traits (i.e., true scores) following a normal distribution and responses to their respective 1/3 and 2/3 items in length; (ii) 20 scenarios of item lengths from 5 to 100; and (iii) 20 sample sizes from 50 to 1000. Each item containing 5-point polytomous responses was uniformly distributed in difficulty across a ± 2 logit range. Three methods (i.e., dimension interrelation ≥0.7, Horn's parallel analysis (PA) 95% confidence interval, and individual random eigenvalues) were used for determining one factor to retain. DC refers to the binary classification (1 as one factor and 0 as many factors) used for examining accuracy with the indicators sensitivity, specificity, and area under receiver operating characteristic curve (AUC). The scale's reliability and DC were simultaneously calculated for each simulative dataset. PSC real data were demonstrated with DC to interpret reports of the unit-based construct validity using the author-made MS Excel module. RESULTS: The DC method presented accurate sensitivity (=0.96), specificity (=0.92) with a DC criterion (≥0.70), and AUC (=0.98) that were higher than those of the two PA methods. PA combined with DC yielded good sensitivity (=0.96), specificity (=1.0) with a DC criterion (≥0.70), and AUC (=0.99). CONCLUSIONS: Advances in computer technology may enable healthcare users familiar with MS Excel to apply DC as a factor retention criterion for determining a scale's unidimensionality and evaluating a scale's quality.

Hospital nurses' attitudes, negative perceptions, and negative acts regarding workplace bullying.

BACKGROUND: Workplace bullying is a prevalent problem in today's work places that has adverse effects on both bullying victims and organizations. To investigate the predictors of workplace bullying is an important task to prevent bullying victims of nurses in hospitals. OBJECTIVE: This study aims to explore the relationships among nurses' attitudes, negative perceptions, and negative acts regarding workplace bullying under the framework of the theory of planned behavior (TPB). METHODS: A total of 811 nurses from three hospitals in Taiwan were surveyed. Nurses' responses to the 201 items of 10 scales were calibrated using Rasch analysis and then subjected to path analysis with partial least-squares structural equation modeling (PLS-SEM). RESULTS: The instrumental attitude was significant predictors of nurses' negative perceptions to be bullied in the workplace. Instead, the other TPB components of subjective norm and perceived behavioral control were not effective predictors of nurses' negative acts regarding workplace bullying. CONCLUSIONS: The findings provided hospital nurse management with important implications for prevention of bullying, particularly to them who are tasked with providing safer and more productive workplaces to hospital nurses. Awareness of workplace bullying was recommended to other kinds of workplaces for further studies in future.

A new technique to measure online bullying: online computerized adaptive testing.

BACKGROUND: Workplace bullying has been measured in many studies to investigate mental health issues. None uses online computerized adaptive testing (CAT) with cutting points to report bully prevalence at workplace. OBJECTIVE: To develop an online CAT to examine person being bullied and verify whether item response theory-based CAT can be applied online for nurses to measure exposure to workplace bullying. METHODS: A total of 963 nurses were recruited and responded to the 22-item Negative Acts Questionnaire-Revised (NAQ-R). All non-adaptive testing (NAT) items were calibrated with the Rasch rating scale model. Three scenarios (i.e., NAT, CAT, and the randomly selected method to NAT) were manipulated to compare their response efficiency and precision by comparing (i) item length for answering questions, person measure, (ii) correlation coefficients, (iii) paired t tests, and (iv) estimated standard errors (SE) between CAT and the random to its counterpart of NAT. RESULTS: The NAQ-R is a unidimensional construct that can be applied for nurses to measure exposure to workplace bullying on CAT. CAT required fewer items (=8.9) than NAT (=22, an efficient gain of 60% =1-8.9/22). Nursing measures derived from both tests (CAT and the random to NAT) were highly correlated (r = 0.93 and 0.96) and their measurement precisions were not statistically different (the percentage of significant count number less than 5%) as expected, but CAT earns smaller person measure SE than the random scenario. The prevalence rate for nurses was 1.5% (=15/963) when cutting points set at -0.7 and 0.7 logits. CONCLUSION: The CAT-based NAQ-R reduces respondents' burden without compromising measurement precision and increases endorsement efficiency. The online CAT is recommended for assessing nurses using the criteria at -0.7 and 0.7 (or <30 and <60 in summed score) to identify bully grade as one of the three levels (high, moderate, and low). The bullied nurse can get help from a psychiatrist or a mental health expert at an earlier stage.

Estimating Skin Cancer Risk: Evaluating Mobile Computer-Adaptive Testing.

BACKGROUND: Response burden is a major detriment to questionnaire completion rates. Computer adaptive testing may offer advantages over non-adaptive testing, including reduction of numbers of items required for precise measurement. OBJECTIVE: Our aim was to compare the efficiency of non-adaptive (NAT) and computer adaptive testing (CAT) facilitated by Partial Credit Model (PCM)-derived calibration to estimate skin cancer risk. METHODS: We used a random sample from a population-based Australian cohort study of skin cancer risk (N=43,794). All 30 items of the skin cancer risk scale were calibrated with the Rasch PCM. A total of 1000 cases generated following a normal distribution (mean [SD] 0 [1]) were simulated using three Rasch models with three fixed-item (dichotomous, rating scale, and partial credit) scenarios, respectively. We calculated the comparative efficiency and precision of CAT and NAT (shortening of questionnaire length and the count difference number ratio less than 5% using independent t tests). RESULTS: We found that use of CAT led to smaller person standard error of the estimated measure than NAT, with substantially higher efficiency but no loss of precision, reducing response burden by 48%, 66%, and 66% for dichotomous, Rating Scale Model, and PCM models, respectively. CONCLUSIONS: CAT-based administrations of the skin cancer risk scale could substantially reduce participant burden without compromising measurement precision. A mobile computer adaptive test was developed to help people efficiently assess their skin cancer risk.

Simulation study of activities of daily living functions using online computerized adaptive testing.

BACKGROUND: Computer adaptive testing (CAT) of the activities of daily living (ADL) functions is required (i) to reveal the advantages of using an efficient and accurate estimation method, (ii) to determine the cutpoint for classifying ADL strata in patients with stroke, and (iii) to evaluate the feasibility of online CAT used in clinical settings for smartphones. METHODS: Normally standardized distributions of ADL measurements were simulated using item parameters from published papers. We retrieved item parameters of the combined Barthel Index and Frenchay Activities Index from the literature (the 23-item comprehensive ADL [CADL] and 34-item ADL scales) and simulated three 1000-person measures from a normal standard CAT distribution: [i] CADL (CADL-CAT), [ii] ADL (ADL-CAT), and [iii] NAT (Non-Adaptive Testing). The cutpoints of ADL person strata were determined using a norm-reference method. Maximum a posteriori estimation, expected a posteriori estimation, and maximum likelihood estimation (MAP) were used to compare the Pearson correlation coefficients and different number ratios of paired measures yielded by CAT and NAT. The number of items and the cutpoints for the scale were separately determined. RESULTS: We found that (i) correlation coefficients for the three CAT-estimated measures were 0.77 (CADL), 0.93 (Male ADL), and 0.93 (Female ADL) compared with their NAT counterparts. Different number ratios of person-paired measures between CAT and NAT for the three scales were all less than 5 %, indicating no difference exists between CAT and NAT. However, CAT might be 66 % more efficient than NAT. (ii) The estimated cutpoints of T scores (i.e., with a mean of 50 and a standard deviation of 10) were 45, 55, and 65 (e.g., separating person ADL function to four strata with not active, fairly active, active, and very active). (iii) An available-for-download online ADL-CAT APP for clinical practice was demonstrated. CONCLUSIONS: An online ADL-CAT APP using the MAP method was created and used on smartphones to classify ADL strata in patients with stroke.

A Meta-Analysis of the Effects of Coping Strategies on Reducing Nurse Burnout.

OBJECTIVE: Nurse burnout is a global issue; however, it remains unclear how coping strategies over a maintained period of time may influence nursing burnout. This meta-analysis aimed to evaluate the literature on the effects of coping strategies in reducing nurse burnout. METHODS: Systematic reviews of English and Chinese articles were conducted for relevant articles published between 1979 and 2014 in six electronic databases (PubMed, CINAHL, The Cochrane, PsycARTICLES, Airiti Library, and the Index of the Taiwan Periodical Literature System). The search terms included 'nurse,' 'burnout' and 'coping.' Studies were included in the review if they were randomized controlled trials or controlled clinical trials, and they used the measurement tool, Maslach Burnout Inventory-Human Service Survey. Three hundred fifty-one studies were identified, and seven studies were ultimately included in the meta-analysis. RESULTS: A total of 1,521 participants were included in the meta-analysis for each burnout subgroup. Participants were measured immediately after the intervention and 6months, 1year, 2years, 2.5years, and 4years thereafter. Coping strategies were hypothesized to decrease burnout. The maintained period for coping strategies was 1year for emotional exhaustion and depersonalization and 6months for personal accomplishment. CONCLUSIONS: Coping strategies can reduce nurse burnout and maintain effectiveness between 6months and 1year.

Predictors of Burnout Among Nurses in Taiwan.

Nurse burnout is a crucial issue for health care professionals and impacts nurse turnover and nursing shortages. Individual and situational factors are related to nurse burnout with predictors of burnout differing among cultures and health care systems. The predictors of nurse burnout in Asia, particularly Taiwan, are unknown. The purpose of this study was to investigate the predictors of burnout among a national sample of nurses in Taiwan. A secondary data analysis of a nationwide database investigated the predictors of burnout among 1,846 nurses in Taiwan. Hierarchical regression analysis determined the relationship between predictors and burnout. Predictors of Taiwanese nurse burnout were age, physical/psychological symptoms, job satisfaction, work engagement, and work environment. The most significant predictors were physical/psychological symptoms and work engagement. The variables explained 35, 39, and 18 % of the emotional exhaustion, personal accomplishment, and depersonalization variance for 54 % of the total variance of burnout. Individual characteristics and nurse self-awareness, especially work, engagement can impact Taiwanese nurses' burnout. Nurse burnout predictors provide administrators with information to develop strategies including education programs and support services to reduce nurse burnout.

Applying computerized adaptive testing to the Negative Acts Questionnaire-Revised: Rasch analysis of workplace bullying.

BACKGROUND: Workplace bullying is a prevalent problem in contemporary work places that has adverse effects on both the victims of bullying and organizations. With the rapid development of computer technology in recent years, there is an urgent need to prove whether item response theory-based computerized adaptive testing (CAT) can be applied to measure exposure to workplace bullying. OBJECTIVE: The purpose of this study was to evaluate the relative efficiency and measurement precision of a CAT-based test for hospital nurses compared to traditional nonadaptive testing (NAT). Under the preliminary conditions of a single domain derived from the scale, a CAT module bullying scale model with polytomously scored items is provided as an example for evaluation purposes. METHODS: A total of 300 nurses were recruited and responded to the 22-item Negative Acts Questionnaire-Revised (NAQ-R). All NAT (or CAT-selected) items were calibrated with the Rasch rating scale model and all respondents were randomly selected for a comparison of the advantages of CAT and NAT in efficiency and precision by paired t tests and the area under the receiver operating characteristic curve (AUROC). RESULTS: The NAQ-R is a unidimensional construct that can be applied to measure exposure to workplace bullying through CAT-based administration. Nursing measures derived from both tests (CAT and NAT) were highly correlated (r=.97) and their measurement precisions were not statistically different (P=.49) as expected. CAT required fewer items than NAT (an efficiency gain of 32%), suggesting a reduced burden for respondents. There were significant differences in work tenure between the 2 groups (bullied and nonbullied) at a cutoff point of 6 years at 1 worksite. An AUROC of 0.75 (95% CI 0.68-0.79) with logits greater than -4.2 (or >30 in summation) was defined as being highly likely bullied in a workplace. CONCLUSIONS: With CAT-based administration of the NAQ-R for nurses, their burden was substantially reduced without compromising measurement precision.

Analyzing shifts in age-related macular degeneration research trends since 2014: A bibliometric study with triple-map Sankey diagrams (TMSD).

BACKGROUND: Age-related macular degeneration (AMD) is the primary cause of vision impairment in older adults, especially in developed countries. While many articles on AMD exist in the literature, none specifically delve into the trends based on document categories. While bibliometric studies typically use dual-map overlays to highlight new trends, these can become congested and unclear with standard formats (e.g., in CiteSpace software). In this study, we introduce a unique triple-map Sankey diagram (TMSD) to assess the evolution of AMD research. Our objective is to understand the nuances of AMD articles and show the effectiveness of TMSD in determining whether AMD research trends have shifted over the past decade. METHODS: We collected 7465 articles and review pieces related to AMD written by ophthalmologists from the Web of Science core collection, accumulating article metadata from 2014 onward. To delve into the characteristics of these AMD articles, we employed various visualization methods, with a special focus on TMSD to track research evolution. We adopted the descriptive, diagnostic, predictive, and prescriptive analytics (DDPP) model, complemented by the follower-leading clustering algorithm (FLCA) for clustering analysis. This synergistic approach proved efficient in identifying and showcasing research focal points and budding trends using network charts within the DDPP framework. RESULTS: Our findings indicate that: in countries, institutes, years, authors, and journals, the dominant entities were the United States, the University of Bonn in Germany, the year 2021, Dr Jae Hui Kim from South Korea, and the journal "Retina"; in accordance with the TMSD, AMD research trends have not changed significantly since 2014, as the top 4 categories for 3 citing, active, and cited articles have not changed, in sequence (Ophthalmology, Science & Technology - Other Topics, General & Internal Medicine, Pharmacology & Pharmacy). CONCLUSION: The introduced TMSD, which incorporates the FLCA algorithm and features in 3 columns-cited, active, and citing research categories-offers readers clearer insights into research developments compared to the traditional dual-map overlays from CiteSpace software. Such tools are especially valuable for streamlining the visualization of the intricate data often seen in bibliometric studies.

Predicting the hyperglycemic crisis death (PHD) score: a new decision rule for emergency and critical care.

BACKGROUND: We investigated independent mortality predictors of hyperglycemic crises and developed a prediction rule for emergency and critical care physicians to classify patients into mortality risk and disposition groups. METHODS: This study was done in a university-affiliated medical center. Consecutive adult patients (> 18 years old) visiting the emergency department (ED) between January 2004 and December 2010 were enrolled when they met the criteria of a hyperglycemic crisis. Data were separated into derivation and validation sets-the former were used to predict the latter. December 31, 2008, was the cutoff date. Thirty-day mortality was the primary endpoint. RESULTS: We enrolled 295 patients who made 330 visits to the ED: derivation set = 235 visits (25 deaths: 10.6%), validation set = 95 visits (10 deaths: 10.5%). We found 6 independent mortality predictors: Absent tachycardia, Hypotension, Anemia, Severe coma, Cancer history, and Infection (AHA.SCI). After assigning weights to each predictor, we developed a Predicting Hyperglycemic crisis Death (PHD) score that stratifies patients into mortality-risk and disposition groups: low (0%) (95% CI, 0-0.02%): treatment in a general ward or the ED; intermediate (24.5%) (95% CI, 14.8-39.9%): the intensive care unit or a general ward; and high (59.5%) (95% CI, 42.2-74.8%): the intensive care unit. The area under the curve for the rule was 0.946 in the derivation set and 0.925 in the validation set. CONCLUSIONS: The PHD score is a simple and rapid rule for predicting 30-day mortality and classifying mortality risk and disposition in adult patients with hyperglycemic crises.

Examining factor structure of Maslach Burnout Inventory among nurses in Taiwan.

AIM: To investigate the factorial structure of a Chinese version of the MBI-HSS for nurses in Taiwan. BACKGROUND Previous studies have presented different factorial structures using the Maslach burnout inventory-human services survey (MBI-HSS). METHODS: Secondary data analysis was implemented to explore the factor structure of MBI-HSS using exploratory factor analysis. Confirmatory factor analysis was then performed to verify the modified structure for nurses in Taiwan. RESULTS: The EFA found that three factors explaining 57% of the variance were extracted, and 20 of the 22 items were retained. The goodness-of-fit test was performed using the CFA approach, and it was verified that the modified version of MBI-HSS is a suitable instrument for measuring burnout for nurses in Taiwan. CONCLUSIONS: A nationwide sample confirmed the factorial structure of MBI-HSS for nurses in Taiwan with a three-dimension, 20-item assessment, and the variance was not diminished in this sample. IMPLICATIONS FOR NURSING MANAGEMENT: These findings demonstrate that the modified version of MBI-HSS provides a suitable instrument for measuring burnout for nurses in Taiwan. Therefore, the modified version of MBI-HSS can be used to compare burnout of nurses across cultures, providing valuable information for policies or preventions in the future.

Comment on the article: The state of social science research on COVID­19.

A well-written and interesting article was published on November 21, 2021. Future relevant studies, however, may be improved by implementing (1) a framework that outlines the overall research; (2) an author-weighted scheme (AWS) that accurately quantifies the contributions of entities to articles; and (3) a more appropriate size for the nodes representing the proportional counts for each entity in social network analysis (SNA). VOSviewer was used to construct and visualize the scientometric networks and the relation-based analyses included three categories: (1) citation relations, (2) word cooccurrences, and (3) coauthorship relations. Nevertheless, the counts for each topical entity have not been consistently integrated. As a result, the nodes of the keyword co-occurrence network are large when compared to the number of connections between the entities or terms (i.e., the total number of relationships between co-occurring terms or entities). Additionally, all weighted counts in keywords (or the total link strength of a country/region) should equal the total number of documents (e.g., n = 9954 in that article). This would lead to biases in the calculation of publications (or citations) for entities, as is common in traditional SNA. This node illustrates a study framework and a couple of AWSs (i.e., equal and nonequal AWSs) to improve the article, and discusses the need to understand the requirement that the total centrality degree in SNA equals the total number of documents (or citations).

Visualizing burst spots on research for four authors in MDPI journals named to be Citation Laureates 2021 using temporal bar graph.

BACKGROUND: The appearance of a topic in a document stream is signaled by a burst of activity, with certain features rising sharply in frequency as the topic emerges. Although temporal bar graph (TBG) is frequently applied to present the burst spot in the bibliographical study, none of the research has combined the inflection point (IP) to interpret the burst spot feature. The aims of this study are to improve the traditional TBG and apply the TBG to understand better the evolution of a topic (e.g., publications and citations for a given author). METHODS: The EISTL model, including entity, indicator, selection of a few vital ones (named attributes) with higher values in quantity (e.g., the citation data of the top 10 entities), TBG and line-chart plots to verify the trend of interest, was proposed to demonstrate the TBG as a whole. The IP locations compared to the median point in data along with the heap map and line-chart trend were identified. The burst strength was computed. A dashboard on Google Maps was designed and launched for bibliometric analysis. Four authors in MDPI (Multidisciplinary Digital Publishing Institute) journals named to be Citation Laureates 2021 were recruited to compare their research achievements shown on the TBG, particularly displaying the burst spots and the recent developments and stages (e.g., increasing, ready to increase, slowdown, or decreasing). RESULTS: We observed that the highest burst strengths in publication and citations are earned by Barry Halliwell (8.99) and Jean-Pierre Changeux (18.01). The breakthrough of TBG using the EISTL model to display the influence of authors in academics was made with 2 parts of the primary IP point and the trend feature in the data. CONCLUSION: The dashboard-type TBG shown on Google Maps is unique and innovative and able to provide deeper insights to readers, not merely limited to the publications and citations for a given author as we did in this study.

Thematic maps with scatter and 4-quadrant plots in R to identity dominant entities on schizophrenia in psychiatry since 2017: Bibliometric analysis.

BACKGROUND: Schizophrenia affects more than 21 million people worldwide. There have been a number of articles published in the literature regarding schizophrenia. It is unclear which authors contributed the most to the field of schizophrenia. This study examines which article entities (affiliated countries, institutes, journals, and authors) earn the most research achievements (RAs) and whether keywords in articles are associated with the number of article citations. METHODS: As of August 25, 2022, 20,606 abstracts published on schizophrenia in psychiatry since 2017 were retrieved from the WoS core collection (WoSCC). RAs were measured using the category, JIF, authorship, and L-index (CJAL) score. The follower-leading cluster algorithm (FLCA) was used to examine clusters of keywords associated with core concepts of research. There were 7 types of visualizations used to report the study results, including Sankey diagrams, choropleth maps, scatter charts, radar plots, and cluster plots. A hypothesis was examined that the mean number of citations for keywords could predict the number of citations for 100 top-cited articles(T100SCHZ). RESULTS: The results indicate that the US (18861), Kings College London (U.S. (2572), Psychiatry (14603), and Kolanu Nithin (Australia) (9.88) had the highest CJAL scores in countries, institutes, departments, and authors, respectively. The journal of Schizophrenia Res had higher citations (19,017), counts (1681), and mean citations (11.31) in journals. There was a significant correlation between article citations and weighted keywords (F = 1471.74; P < .001). CONCLUSION: Seven visualizations were presented to report the study results, particularly with thematic maps using scatter and 4-quadrant plots produced in R programming language. We recommend that more future bibliographical studies utilize CAJL scores and thematic maps to report their findings, not restrict themselves solely to schizophrenia in psychiatry as done in this study.

Circle packing charts generated by ChatGPT to identify the characteristics of articles by anesthesiology authors in 2022: Bibliometric analysis.

BACKGROUND: The ChatGPT (Open AI, San Francisco, CA), denoted by the Chat Generative Pretrained Transformer, has been a hot topic for discussion over the past few months. A verification of whether the code for drawing circle packing charts (CPCs) with R can be generated by ChatGPT and used to identify characteristics of articles by anesthesiology authors is needed. This study aimed to provide insights into article characteristics in the field of anesthesiology and to highlight the potential of ChatGPT for data visualization techniques (e.g., CPCs) in bibliometric analysis. METHODS: A total of 23,012 articles were indexed in PubMed in 2022 by authors in the field of anesthesiology. The code for drawing CPCs with R was generated by ChatGPT and then modified by the authors to identify the characteristics of articles in 2 forms: 23,012 and 100 top-impact factors in journals (T100IF). Using CPCs and 3 other visualizations-network charts, impact beam plots, and Sankey diagrams-we were able to display article features commonly used in bibliometric analysis. The author-weighted scheme and absolute advantage coefficient were used to assess dominant entities, such as countries, institutes, authors, and themes (defined by PubMed and MeSH terms). RESULTS: Our findings indicate that: further modifications should be made to the code generated by ChatGPT for drawing CPCs in R; publications in the field of anesthesiology are dominated by China, followed by the United States and Japan; Capital Medical University (China) and Showa University Hospital (Japan) dominate research institutes in terms of publications and IF, respectively; and COVID-19 is the most frequently reported theme in T100IF, accounting for 29%. CONCLUSIONS: No such articles with CPCs regarding bibliometrics have ever been found in PubMed. The code for drawing CPCs with R can be generated by ChatGPT, but further modification is required for implementation in bibliometrics. CPCs should be used in future studies to identify the characteristics of articles in other areas of research rather than limiting them to anesthesiology, as we did in this study.