bims-librar Biomed News
on Biomedical librarianship
Issue of 2019‒09‒22
twelve papers selected by
Thomas Krichel
Open Library Society


  1. Health Info Libr J. 2019 Sep;36(3): 278-282
    Butler R.
      This paper is based on Rachel Butler's dissertation carried out at the University of Sheffield as part of the MA Library and Information Services Management. The study examines people's online health information seeking skills, with the specific aim to identify how libraries and health services can work together in supporting digital and health literacy. A survey approach is used to explore online searching habits as well as librarian and health professionals' views on health literacy. The key findings indicate that whilst the majority of respondents consider themselves to be health literate, there was an overall agreement that effective education and support could be achieved through the collaboration between libraries and health services, and specifically to signpost information and to provide targeted education. The limitations of the research for dissertation are recognised leading to recommendations that further study focuses on the impact of signposting and education on health literacy.F.J.
    Keywords:  health literacy; information seeking behaviour; internet access; librarians
    DOI:  https://doi.org/10.1111/hir.12278
  2. Health Info Libr J. 2019 Sep;36(3): 199-201
    Pratchett T.
      Tracey Pratchett considers the emerging role for libraries in facilitating document management practices within NHS organisations and reflects on her experience of supporting this work at Lancashire Teaching Hospitals NHS Foundation Trust.
    Keywords:  National Health Service; governance; hospitals, teaching
    DOI:  https://doi.org/10.1111/hir.12277
  3. Health Info Libr J. 2019 Sep;36(3): 288-293
    George S, Rowland J.
      This feature suggests that health librarians who teach or support Higher Education (HE) students can and should gain accreditation and recognition for their teaching by the route of HEA Fellowship. We outline the process by which Fellowship could be attained by those working within HE and those in NHS libraries who work with HE students, suggesting which aspects of librarianship practice could provide the necessary evidence for Fellowship. The synergies between Fellowship and Chartership are examined and the criteria for HEA (UK Professional Standards Framework or UKPSF) are mapped against those for Chartership (Professional Knowledge and Skills Base (PKSB). D.I.
    Keywords:  careers; education and training; higher education; hospitals, teaching; information literacy; libraries, academic; libraries, health care; teaching
    DOI:  https://doi.org/10.1111/hir.12272
  4. Health Info Libr J. 2019 Sep;36(3): 283-287
    Chande-Mallya R.
      This article is part of a series in this regular feature, which looks at new directions in health science libraries. It highlights the initiatives health science librarians in Tanzania are implementing to ensure that their service meets users' needs. To succeed, libraries must take steps to ensure that staff have access to education, training and professional development. Partnerships and collaborations are also vital to make the best use of scarce resources and to identify sources of funding. This article highlights the various challenges facing the library service and the opportunity for librarians to be recognised for the new roles they are taking on. J.M.
    Keywords:  Africa, East; ICT training; case reports; collaboration; education and training; librarianship, health science; literacy programmes; national strategies; professional associations
    DOI:  https://doi.org/10.1111/hir.12271
  5. Patient Educ Couns. 2019 Aug 28. pii: S0738-3991(19)30381-7. [Epub ahead of print]
    Jensen JD, Pokharel M, Carcioppolo N, Upshaw S, John KK, Katz RA.
      OBJECTIVE: Past research suggests a large number of adults feel overwhelmed by the amount of cancer information - a phenomenon labeled cancer information overload (CIO). The current study examines whether CIO is discriminant from other negative message perceptions (reactance, information avoidance) and related to sun safe behaviors.METHODS: U.S. adults (N = 2,219) completed survey questions assessing CIO, dispositional reactance, defensive/information avoidance, sun safe behavior, and knowledge.
    RESULTS: The results demonstrated that CIO was discriminant from dispositional reactance, information avoidance, and defensive avoidance, and individuals with higher overload were more likely to tan, less likely to have an annual checkup with a healthcare provider, and less knowledgeable about sun safe protection. Unexpectedly, individuals with higher CIO were more likely to wear wide-brimmed hats.
    CONCLUSION: CIO is distinct from reactance and avoidance, and related to performance/knowledge of sun safe behaviors, and receiving annual healthcare checkups.
    PRACTICE IMPLICATIONS: The correlation between CIO and sun safe behavior differs by behavior; a pattern which suggests practitioners might benefit from adapting their communication strategy based on the target population and behavior.
    Keywords:  Cancer information overload; Defensive avoidance; Discriminant validity; Dispositional reactance; Information avoidance; Sun safe behavior; Tanning
    DOI:  https://doi.org/10.1016/j.pec.2019.08.039
  6. Artif Intell Med. 2019 Jul;pii: S0933-3657(18)30491-3. [Epub ahead of print]98 18-26
    Tran T, Kavuluru R.
      The growing body of knowledge in biomedicine is too vast for human consumption. Hence there is a need for automated systems able to navigate and distill the emerging wealth of information. One fundamental task to that end is relation extraction, whereby linguistic expressions of semantic relationships between biomedical entities are recognized and extracted. In this study, we propose a novel distant supervision approach for relation extraction of binary treatment relationships such that high quality positive/negative training examples are generated from PubMed abstracts by leveraging associated MeSH subheadings. The quality of generated examples is assessed based on the quality of supervised models they induce; that is, the mean performance of trained models (derived via bootstrapped ensembling) on a gold standard test set is used as a proxy for data quality. We show that our approach is preferable to traditional distant supervision for treatment relations and is closer to human crowd annotations in terms of annotation quality. For treatment relations, our generated training data performs at 81.38%, compared to traditional distant supervision at 64.33% and crowd-sourced annotations at 90.57% on the model-wide PR-AUC metric. We also demonstrate that examples generated using our method can be used to augment crowd-sourced datasets. Augmented models improve over non-augmented models by more than two absolute points on the more established F1 metric. We lastly demonstrate that performance can be further improved by implementing a classification loss that is resistant to label noise.
    Keywords:  Distant supervision; MeSH subheadings; Medical treatment relation; Relation extraction
    DOI:  https://doi.org/10.1016/j.artmed.2019.06.002
  7. NPJ Digit Med. 2019 ;2 90
    Kamdar MR, Fernández JD, Polleres A, Tudorache T, Musen MA.
      The biomedical data landscape is fragmented with several isolated, heterogeneous data and knowledge sources, which use varying formats, syntaxes, schemas, and entity notations, existing on the Web. Biomedical researchers face severe logistical and technical challenges to query, integrate, analyze, and visualize data from multiple diverse sources in the context of available biomedical knowledge. Semantic Web technologies and Linked Data principles may aid toward Web-scale semantic processing and data integration in biomedicine. The biomedical research community has been one of the earliest adopters of these technologies and principles to publish data and knowledge on the Web as linked graphs and ontologies, hence creating the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we provide our perspective on some opportunities proffered by the use of LSLOD to integrate biomedical data and knowledge in three domains: (1) pharmacology, (2) cancer research, and (3) infectious diseases. We will discuss some of the major challenges that hinder the wide-spread use and consumption of LSLOD by the biomedical research community. Finally, we provide a few technical solutions and insights that can address these challenges. Eventually, LSLOD can enable the development of scalable, intelligent infrastructures that support artificial intelligence methods for augmenting human intelligence to achieve better clinical outcomes for patients, to enhance the quality of biomedical research, and to improve our understanding of living systems.
    Keywords:  Computational platforms and environments; Data integration; Databases
    DOI:  https://doi.org/10.1038/s41746-019-0162-5
  8. J Biomed Semantics. 2019 Sep 18. 10(1): 15
    Kafkas Ş, Hoehndorf R.
      BACKGROUND: Infectious diseases claim millions of lives especially in the developing countries each year. Identification of causative pathogens accurately and rapidly plays a key role in the success of treatment. To support infectious disease research and mechanisms of infection, there is a need for an open resource on pathogen-disease associations that can be utilized in computational studies. A large number of pathogen-disease associations is available from the literature in unstructured form and we need automated methods to extract the data.RESULTS: We developed a text mining system designed for extracting pathogen-disease relations from literature. Our approach utilizes background knowledge from an ontology and statistical methods for extracting associations between pathogens and diseases. In total, we extracted a total of 3420 pathogen-disease associations from literature. We integrated our literature-derived associations into a database which links pathogens to their phenotypes for supporting infectious disease research.
    CONCLUSIONS: To the best of our knowledge, we present the first study focusing on extracting pathogen-disease associations from publications. We believe the text mined data can be utilized as a valuable resource for infectious disease research. All the data is publicly available from https://github.com/bio-ontology-research-group/padimi and through a public SPARQL endpoint from http://patho.phenomebrowser.net/ .
    Keywords:  Infectious disease; Pathogen; Pathogen–disease association; Relationship extraction; Text mining
    DOI:  https://doi.org/10.1186/s13326-019-0208-2
  9. Health Info Libr J. 2019 Sep;36(3): 202-222
    Sutton A, Clowes M, Preston L, Booth A.
      BACKGROUND AND OBJECTIVES: The last decade has witnessed increased recognition of the value of literature reviews for advancing understanding and decision making. This has been accompanied by an expansion in the range of methodological approaches and types of review. However, there remains uncertainty over definitions and search requirements beyond those for the 'traditional' systematic review. This study aims to characterise health related reviews by type and to provide recommendations on appropriate methods of information retrieval based on the available guidance.METHODS: A list of review types was generated from published typologies and categorised into 'families' based on their common features. Guidance on information retrieval for each review type was identified by searching pubmed, medline and Google Scholar, supplemented by scrutinising websites of review producing organisations.
    RESULTS: Forty-eight review types were identified and categorised into seven families. Published guidance reveals increasing specification of methods for information retrieval; however, much of it remains generic with many review types lacking explicit requirements for the identification of evidence.
    CONCLUSIONS: Defining review types and utilising appropriate search methods remain challenging. By familiarising themselves with a range of review methodologies and associated search methods, information specialists will be better equipped to select suitable approaches for future projects.
    Keywords:  information retrieval; information science; literature searching; overview; search strategies
    DOI:  https://doi.org/10.1111/hir.12276
  10. Interact J Med Res. 2019 Sep 16. 8(3): e12855
    Murray KE, Murray TE, O'Rourke AC, Low C, Veale DJ.
      BACKGROUND: Osteoarthritis (OA) is the most common cause of disability in people older than 65 years. Readability of online OA information has never been assessed. A 2003 study found the quality of online OA information to be poor.OBJECTIVE: The aim of this study was to review the readability and quality of current online information regarding OA.
    METHODS: The term osteoarthritis was searched across the three most popular English language search engines. The first 25 pages from each search engine were analyzed. Duplicate pages, websites featuring paid advertisements, inaccessible pages (behind a pay wall, not available for geographical reasons), and nontext pages were excluded. Readability was measured using Flesch Reading Ease Score, Flesch-Kincaid Grade Level, and Gunning-Fog Index. Website quality was scored using the Journal of the American Medical Association (JAMA) benchmark criteria and the DISCERN criteria. Presence or absence of the Health On the Net Foundation Code of Conduct (HONcode) certification, age of content, content producer, and author characteristics were noted.
    RESULTS: A total of 37 unique websites were found suitable for analysis. Readability varied by assessment tool from 8th to 12th grade level. This compares with the recommended 7th to 8th grade level. Of the 37, 1 (2.7%) website met all 4 JAMA criteria. Mean DISCERN quality of information for OA websites was "fair," compared with the "poor" grading of a 2003 study. HONcode-endorsed websites (43%, 16/37) were of a statistically significant higher quality.
    CONCLUSIONS: Readability of online health information for OA was either equal to or more difficult than the recommended level.
    Keywords:  arthritis; internet; osteoarthritis; patient; readability
    DOI:  https://doi.org/10.2196/12855
  11. Fa Yi Xue Za Zhi. 2019 Aug;35(4): 423-427
    Xie XP, Pan ZJ, Wang K, Yu YX, Liang M.
      Abstract: Objective To analyze a knowledge web of the literature published by Journal of Forensic Medicine from its founding in 1985 to 2018, describe the evolving process of forensic science research and explore the research hotspots and frontiers at present. Methods The literature that was published by Journal of Forensic Medicine from 1985 to 2018 was collected and analyzed in terms of elements, such as emerging research hotspots, high frequency keywords, authors, dispatching units, location of institution and funding, by CiteSpace5.3 information visualization analysis software. Results All disciplines of forensic medicine were continually developing and maturing, and the publication volume of the literature on forensic pathology had the highest weight; in research hotspots, the two categories, research and identification each had their own emphasis; as the main source of contributions to the journal, research institutes accounted for 38.99% of the total number of publications; Shanghai ranked first among all regions with 1 046 articles published. The number of funded articles was generally on the rise, with the number of funded articles published largest in 2015. Conclusion As an authoritative academic journal in the field of forensic science in China, Journal of Forensic Medicine carries the development of forensic science and witnesses the institutional reform of universities and colleges, and offers a wide range of communication and cooperation in terms of technicality and application. Many scholars and scientific research institutions have gained progress continually in various research directions in the form of teamwork; and emerging research hotspots will continue to play a huge role in future practical applications.
    Keywords:  forensic medicine; bibliometrics; visualization analysis; CiteSpace; Journal of Forensic Medicine
    DOI:  https://doi.org/10.12116/j.issn.1004-5619.2019.04.008