bims-librar Biomed News
on Biomedical librarianship
Issue of 2020‒03‒29
six papers selected by
Thomas Krichel
Open Library Society


  1. Travel Med Infect Dis. 2020 Mar 20. pii: S1477-8939(20)30104-6. [Epub ahead of print] 101636
    Khatri P, Singh S, Belani NK, Leng YY, Lohan R, Wei LY, Teo WZ.
      BACKGROUND: The current 2019 novel coronavirus outbreak is rapidly evolving. YouTube has been recognized as a popular source of information in previous disease outbreaks. We analyzed the content on YouTube about n-CoV in English and Mandarin languages.METHODS: YouTube was searched using the terms '2019 novel coronavirus', 'Wuhan virus' and '' (Mandarin for Wuhan virus) on 1st and 2nd February 2020. First 50 videos in each group were analyzed. Videos in other languages, duplicate videos, those without an audio and duration >15 min were excluded .72 videos in English and 42 in Mandarin were reviewed. 2 reviewers classified the videos as useful, misleading or news based on pre specified criterion. Inter-observer agreement was evaluated with kappa coefficient. Modified DISCERN index for reliability and medical information and content index (MICI) score were used for content analysis.
    RESULTS: These videos attracted cumulative 21,288,856 views. 67% of English and 50% Mandarin videos had useful information. The viewership of misleading Mandarin videos was higher than the useful ones. WHO accounted for only 4% of useful videos. Mean DISCERN score for reliability was 3.12/5 and 3.25/5 for English and Mandarin videos respectively. Mean cumulative MICI score of useful videos was low (6.71/25 for English and 6.28/25 for Mandarin).
    CONCLUSIONS: YouTube viewership during 2019 n-CoV outbreak is higher than previous outbreaks. The medical content of videos is suboptimal International health agencies are underrepresented. Given its popularity, YouTube should be considered as important platform for information dissemination.
    Keywords:  2019 novel corona virus; Disease outbreak; Internet; Wuhan virus; YouTube
    DOI:  https://doi.org/10.1016/j.tmaid.2020.101636
  2. JMIR Public Health Surveill. 2020 Mar 27.
    Hernández-García I, Giménez-Júlvez T.
      BACKGROUND: The Internet is a large source of health information, and it has the capacity to influence its users. However, the information found on the Internet often lacks scientific rigor, as anyone may upload its content. This factor is a cause of great concern to scientific societies, governments, and users.OBJECTIVE: The objective of our study was to investigate the information about the prevention of coronavirus disease 2019 (COVID-19) on the Internet.
    METHODS: On 2020-02-29 we performed a Google search with the terms "Prevention coronavirus", "Prevention COVID-19", "Prevención coronavirus", and "Prevención COVID-19". A univariate analysis was performed to study the association between the type of authorship, and country of publication, and recommendations to avoid COVID-19 according to the World Health Organization.
    RESULTS: In total, 80 weblinks were reviewed. Most of them were produced in the USA and Spain (72.5%), by digital media and official public health organizations (75.1%). The most mentioned WHO preventive measure was "wash your hands frequently" (81.3%). Less frequent recommendation was related to "stay home if you feel unwell" (32.5%). The analysis by type of author (official public health organizations versus digital media) revealed significant differences regarding the recommendation to wear a mask if you are healthy only if caring for a person with suspected COVID-19 (OR = 4.39). According to country of publication (Spain versus the USA) significant differences were detected regarding some recommendations, such as "wash your hands frequently" (OR = 9.82), "cover your mouth and nose with your bent elbow or tissue when you cough or sneeze" (OR = 4.59), or "stay home if you feel unwell" (OR = 0.31).
    CONCLUSIONS: It is necessary to urge and promote the use of the websites of official public health organizations when seeking information on COVID-19 preventive measures on the Internet. In this way, they will be able to obtain high-quality information more frequently, and such websites may improve their accessibility and positioning given that search engines justify the positioning of links obtained in a search based on the frequency of access to them.
    DOI:  https://doi.org/10.2196/18717
  3. Can Urol Assoc J. 2020 Mar 23.
    Yeo S, Eigl B, Ingledew PA.
      INTRODUCTION: Testicular cancer is the most common solid malignancy diagnosed in young men aged 15-29. This population is also the age group that searches most actively for health information online. This study systematically evaluates the quality of websites available to patients with testicular cancer.METHODS: The term "testicular cancer" was inputted into the search engines Google, Dogpile, and Yippy. The top 100 websites intended for patient education were compiled. A validated structural rating tool was used to evaluate the websites with respect to attribution, currency, disclosure, interactivity, readability, and content.
    RESULTS: Less than half of the websites (44) disclosed authorship. Sixty-one websites provided a last modified date, and of those, 46 were updated in the last two years. The average readability level was 11.01 using the Flesh Kincaid grade level system. The most accurate topic was treatment, with 82 websites being completely accurate and containing all required information. The least accurate topic was prognosis, with 27 being completely accurate.
    CONCLUSIONS: These results show that authorship and currency are lacking in many online testicular resources, making it difficult for patients to validate the reliability of information. The high average readability of testicular cancer websites can affect comprehension. Topics such as prognosis were incompletely covered although represent an area for which patients often seek more information. These results can be used to counsel patients on the strength and weaknesses of online testicular cancer resources.
    DOI:  https://doi.org/10.5489/cuaj.6154
  4. J Med Internet Res. 2020 Mar 24.
    Maitz E, Maitz K, Sendlhofer G, Wolfsberger C, Mautner S, Kamolz LP, Gasteiger-Klicpera B.
      BACKGROUND: Many children and adolescents are surrounded by smartphones, tablets and computers and know how to search the Internet on almost any topic. However, very few of them know how to select proper information from reliable sources. This can become a problem when health issues are concerned where it is vital to identify incorrect or misleading information. The competence to critically evaluate digital information on health issues is of increasing importance for adolescents.OBJECTIVE: The aim of the present study was to assess how children and adolescents rate their online health literacy, how their actual literacy differs from their rating, including the question how their search performance is related to their self-efficacy. To evaluate these questions a criteria-based analysis of the quality of the websites they visited is necessary. Finally, the possibility to increase their online health literacy in a 3-day workshop should be explored.
    METHODS: A workshop with a focus on health literacy was attended by 14 children and adolescents in an Austrian secondary school. After a prior assessment (CFT 20-R, LGVT 6-12, eHEALS, WIRKALL_r), the students were asked to do an Internet-based search on a health-related issue. Browser histories and screenshots of all Internet searches were gathered, clustered and analyzed. After the workshop, the health literacy of the students was assessed again by using the eHealth Literacy Scale (eHEALS).
    RESULTS: Fourteen students opened a total of 85 homepages but only 8 of them were rated as good or fair by an independent rating of two experts based on specific criteria. The analysis showed that the students assessed their own online health literacy much better than it actually was, and students who had rated themselves better did not visit websites of higher quality. Online health literacy correlated significantly with self-efficacy of the students (rs =.794, P =.002).
    CONCLUSIONS: Our study showed that it is possible to draw the attention of students to critical aspects of Internet search as well as to slightly improve their respective competence in a workshop. A targeted improvement of health literacy is urgently needed and students need special instruction for that purpose. Further investigations in this area with larger sets of data, feasible with the help of a computer program, are urgently needed.
    CLINICALTRIAL:
    DOI:  https://doi.org/10.2196/16281
  5. Health Promot Int. 2020 Mar 24. pii: daaa017. [Epub ahead of print]
    Yang Q, Wu S.
      Haze has become one of the most life-threatening problems in China. Chinese people become more dependent on receiving health information from social media, especially WeChat, which shapes their health perceptions and behaviors. Despite the prevalence of health information-seeking behavior (HISB) on WeChat, the predicting factors and consequences of Chinese people's haze HISB using WeChat remain unclear. To fill this gap, a hypothesized model was proposed under the risk perception attitude framework and tested with a longitudinal web-based survey of Chinese people residing in Mainland China, to understand the antecedents and behavioral outcomes of HISB on WeChat. The results from the structural equation modeling showed that perceived risk significantly predicted haze HISB on WeChat, which predicted the intention of wearing PM2.5 mask but not reducing outdoor exercises. The efficacy beliefs of both protective behaviors were not significant predictor of haze HISB. Theoretical and practical implications were discussed.
    Keywords:  WeChat; air pollution; health information seeking; health-protective behavior
    DOI:  https://doi.org/10.1093/heapro/daaa017
  6. Environ Int. 2020 Mar 20. pii: S0160-4120(19)31402-3. [Epub ahead of print]138 105623
    Howard BE, Phillips J, Tandon A, Maharana A, Elmore R, Mav D, Sedykh A, Thayer K, Merrick BA, Walker V, Rooney A, Shah RR.
      BACKGROUND: In the screening phase of systematic review, researchers use detailed inclusion/exclusion criteria to decide whether each article in a set of candidate articles is relevant to the research question under consideration. A typical review may require screening thousands or tens of thousands of articles in and can utilize hundreds of person-hours of labor.METHODS: Here we introduce SWIFT-Active Screener, a web-based, collaborative systematic review software application, designed to reduce the overall screening burden required during this resource-intensive phase of the review process. To prioritize articles for review, SWIFT-Active Screener uses active learning, a type of machine learning that incorporates user feedback during screening. Meanwhile, a negative binomial model is employed to estimate the number of relevant articles remaining in the unscreened document list. Using a simulation involving 26 diverse systematic review datasets that were previously screened by reviewers, we evaluated both the document prioritization and recall estimation methods.
    RESULTS: On average, 95% of the relevant articles were identified after screening only 40% of the total reference list. In the 5 document sets with 5,000 or more references, 95% recall was achieved after screening only 34% of the available references, on average. Furthermore, the recall estimator we have proposed provides a useful, conservative estimate of the percentage of relevant documents identified during the screening process.
    CONCLUSION: SWIFT-Active Screener can result in significant time savings compared to traditional screening and the savings are increased for larger project sizes. Moreover, the integration of explicit recall estimation during screening solves an important challenge faced by all machine learning systems for document screening: when to stop screening a prioritized reference list. The software is currently available in the form of a multi-user, collaborative, online web application.
    Keywords:  Active learning; Document screening; Evidence mapping; Machine learning; Recall estimation; Systematic review
    DOI:  https://doi.org/10.1016/j.envint.2020.105623