The shock of reality that nursing students face when they start out will affect the nursing profession even more in the future, as it faces a recruitment crisis in the midst of renewal. Restoring meaning to the nursing profession is a complex and daunting challenge. By providing access to scientific literature, the bibliography group can contribute to this, based on an Evidence-Based Nursing approach. This initiative, which is beneficial for professionals whose skills development is thus encouraged, is designed to be simple and accessible to as many people as possible.

BACKGROUND: Despite the increasing risks and complexity of disasters, education for Malaysian health care providers in this domain is limited. This study aims to assess scholarly publications by Malaysian scholars on Disaster Medicine (DM)-related topics.
METHODS: An electronic search of five selected journals from 1991 through 2021 utilizing multiple keywords relevant to DM was conducted for review and analysis.
RESULTS: A total of 154 articles were included for analysis. The mean number of publications per year from 1991 through 2021 was 5.1 publications. Short reports were the most common research type (53.2%), followed by original research (32.4%) and case reports (12.3%). Mean citations among the included articles were 12.4 citations. Most author collaborations were within the same agency or institution, and there was no correlation between the type of collaboration and the number of citations (P = .942). While a few clusters of scholars could build a strong network across institutions, most research currently conducted in DM was within small, isolated clusters.
CONCLUSIONS: Disaster Medicine in Malaysia is a growing medical subspecialty with a significant recent surge in research activity, likely due to the SARS-CoV-2/coronavirus disease 2019 (COVID-19) global pandemic. Since most publications in DM have been on infectious diseases, the need to expand DM-related research on other topics is essential.

Biological taxonomy rests on a long tail of publications spanning nearly three centuries. Not only is this literature vital to resolving disputes about taxonomy and nomenclature, for many species it represents a key source-indeed sometimes the only source-of information about that species. Unlike other disciplines such as biomedicine, the taxonomic community lacks a centralised, curated literature database (the \"bibliography of life\"). This article argues that Wikidata can be that database as it has flexible and sophisticated models of bibliographic information, and an active community of people and programs (\"bots\") adding, editing, and curating that information.

Retracted articles represent research withdrawn from the existing body of literature after publication. Research articles may be retracted for several reasons ranging from honest errors to intentional misconduct. They should not be used as reliable sources, and it is unclear why they are cited occasionally by other articles. This study hypothesized that several mechanisms may contribute to citing retracted literature and aimed to analyze the characteristics of articles citing retracted literature in anesthesiology and critical care.
Using the Retraction Watch database, we retrieved retracted articles on anesthesiology and intensive care medicine up to August 16, 2021, and identified the papers citing these retracted articles. A survey designed to investigate the reasons for citing these articles was sent to the corresponding authors of the citing papers.
We identified 478 retracted articles, 220 (46%) of which were cited at least once. We contacted 1297 corresponding authors of the papers that cited these articles, 417 (30%) of whom responded to our survey and were included in the final analysis. The median number of authors in the analyzed articles was five, and the median elapsed time from retraction to citation was 3 yr. Most of the corresponding authors (372, 89%) were unaware of the retracted status of the cited article, mainly because of inadequate notification of the retraction status in journals and/or databases and the use of stored copies.
The corresponding authors were generally unaware of the retraction of the cited article, usually because of inadequate identification of the retracted status in journals and/or web databases and the use of stored copies. Awareness of this phenomenon and rigorous control of the cited references before submitting a paper are of fundamental importance in research.

During the coronavirus disease 2019 (COVID-19) pandemic, publications on the disease have exploded globally. The present study analyzed PubMed and KoreaMed indexed COVID-19 publications by Korean researchers from January 1, 2020 to August 19, 2021. A total of 83,549 COVID-19 articles were recorded in PubMed and 1,875 of these were published by Korean authors in 673 journals (67 Korean and 606 overseas journals). The KoreaMed platform covered 766 articles on COVID-19, including 612 by Korean authors. Among the Journal of Korean Medical Science (JKMS) articles on COVID-19, PubMed covered 176 and KoreaMed 141 documents. Korean researchers contributed to 2.2% of global publications on COVID-19 in PubMed. The JKMS has published most articles on COVID-19 in Korea.

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The breakthrough potentials of research papers can be explained by their boundary-spanning qualities. Here, for the first time, we apply the structural variation analysis (SVA) model and its affiliated metrics to investigate the extent to which such qualities characterize a group of Nobel Prize winning papers. We find that these papers share remarkable boundary-spanning traits, marked by exceptional abilities to connect disparate and topically-diverse clusters of research papers. Further, their publications exert structural variations on a scale that significantly alters the betweenness centrality distributions in existing intellectual space. Overall, SVA not only provides a set of leading indicators for describing future Nobel Prize winning papers, but also broadens our understanding of similar prize-winning properties that may have been overlooked among other regular publications.

But how do we get there from here?

In 1997, lanbotulinumtoxinA (LAN) was introduced in China. It is now available in Asia, Latin America and Eastern Europe under various brand names including Hengli®, Lantox®, Prosigne®, Lanzox®, Redux®, Liftox®, HBTX-A and CBTX-A. The literature on LAN is mostly published in Chinese language, restricting its international accessibility. We, therefore, wanted to generate a complete English bibliography of all LAN publications and then use it for a comprehensive formalised literature review. Altogether, 379 LAN publications (322 in Chinese and 57 in English) were retrieved from PubMed and Science and Technology Paper Citation Database. Indications covered are motor (257), glandular (16), pain (32) and aesthetics (48). Topics are neurological (250), aesthetic (48), paediatric (38), ophthalmological (18), urological (9), methodological (6), gastroenterological (5), ear, nose and throat (4) and surgical (1). Seventy-one publications are randomised controlled trials, forty-one publications are interventional studies and observational studies, fifteen publications are case studies, eighteen publications are reviews, and two publications are guidelines. LAN publications cover all relevant topics of BT therapy throughout a period of more than 20 years. This constitutes a publication basis resembling those of other BT drugs. None of the LAN publications presents data contradictory to those generated with other BT type-A drugs. LAN seems to have a similar efficacy and safety features when compared to onabotulinumtoxinA using a 1:1 LAN- onabotulinumtoxinA conversion ratio. Large controlled multicentre studies will become necessary for LAN\'s registrations in Europe and North America.

Classification schemes for scientific activity and publications underpin a large swath of research evaluation practices at the organizational, governmental, and national levels. Several research classifications are currently in use, and they require continuous work as new classification techniques becomes available and as new research topics emerge. Convolutional neural networks, a subset of \"deep learning\" approaches, have recently offered novel and highly performant methods for classifying voluminous corpora of text. This article benchmarks a deep learning classification technique on more than 40 million scientific articles and on tens of thousands of scholarly journals. The comparison is performed against bibliographic coupling-, direct citation-, and manual-based classifications-the established and most widely used approaches in the field of bibliometrics, and by extension, in many science and innovation policy activities such as grant competition management. The results reveal that the performance of this first iteration of a deep learning approach is equivalent to the graph-based bibliometric approaches. All methods presented are also on par with manual classification. Somewhat surprisingly, no machine learning approaches were found to clearly outperform the simple label propagation approach that is direct citation. In conclusion, deep learning is promising because it performed just as well as the other approaches but has more flexibility to be further improved. For example, a deep neural network incorporating information from the citation network is likely to hold the key to an even better classification algorithm.