bims-librar Biomed News
on Biomedical librarianship
Issue of 2022–09–04
seventeen papers selected by
Thomas Krichel, Open Library Society
  1. Towards a machine-readable literature: finding relevant papers based on an uploaded powder diffraction pattern.
  2. Improvement of intervention information detection for automated clinical literature screening during systematic review.
  3. Application of Digital Information Technology in Book Classification and Quick Search in University Libraries.
  4. Predicting Emerging Themes in Rapidly Expanding COVID-19 Literature with Unsupervised Word Embeddings and Machine Learning.
  5. Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations.
  6. Reading habits of medical practitioners: Young doctors in Pakistan, a case study.
  7. Research on Literature Clustering Algorithm for Massive Scientific and Technical Literature Query Service.
  8. Laypeople's Online Health Information Search Strategies and Use for Health-Related Problems: Cross-sectional Survey.
  9. Patient Education Materials Found via Google Search for Shoulder Arthroscopy Are Written at Too-High of a Reading Level.
  10. Assessing the Quality of YouTube Videos on Adhesive Capsulitis.
  11. Educational quality and content of YouTube videos on diabetic macular edema.
  12. Improving health literacy with mumps, measles and rubella (MMR) vaccination: comparison of the readability of MMR patient-facing literature and MMR scientific abstracts.
  13. Cluster headache - The worst possible pain on YouTube.
  14. Infodemiology for oral health and disease: A scoping review.
  15. Assessing on-line medical education resources: A primer for acute care medical professionals and others.
  16. Perceived accessibility, and adequacy of COVID-19 related information in Nigeria.
  17. COVID-19 health information sources and vaccine hesitancy: Correspondence.
  1. Acta Crystallogr A Found Adv. 2022 Sep 01. 78(Pt 5): 386-394
    Towards a machine-readable literature: finding relevant papers based on an uploaded powder diffraction pattern.
    Berrak Özer, Martin A Karlsen, Zachary Thatcher, Ling Lan, Brian McMahon, Peter R Strickland, Simon P Westrip, Koh S Sang, David G Billing, Dorthe B Ravnsbæk, Simon J L Billinge.
      A prototype application for machine-readable literature is investigated. The program is called pyDataRecognition and serves as an example of a data-driven literature search, where the literature search query is an experimental data set provided by the user. The user uploads a powder pattern together with the radiation wavelength. The program compares the user data to a database of existing powder patterns associated with published papers and produces a rank ordered according to their similarity score. The program returns the digital object identifier and full reference of top-ranked papers together with a stack plot of the user data alongside the top-five database entries. The paper describes the approach and explores successes and challenges.
    Keywords:  CIF; data similarity; data-driven literature search; machine-readable scientific literature; powder diffraction
    DOI:  https://doi.org/10.1107/S2053273322007483
  2. J Biomed Inform. 2022 Aug 26. pii: S1532-0464(22)00194-0. [Epub ahead of print] 104185
    Improvement of intervention information detection for automated clinical literature screening during systematic review.
    Tadashi Tsubota, Danushka Bollegala, Yang Zhao, Yingzi Jin, Tomotake Kozu.
      Systematic literature review (SLR) is a crucial method for clinicians and policymakers to make their decisions in a flood of new clinical studies. Because manual literature screening in SLR is a highly laborious task, its automation by natural language processing (NLP) has been welcomed. Although intervention is a key information for literature screening, NLP models for its detection in previous works have not shown adequate performance. In this work, we first design an algorithm for automated construction of high-quality intervention labels by utilizing information retrieved from a clinical trial database. We then design another algorithm for improving model's recall and F1 score by imposing adaptive weights on training instances in the loss function. The intervention detection model trained on the weighted datasets is tested with the Evidence-Based Medicine NLP (EBM-NLP) corpus, and shows 9.7% and 4.0% improvements respectively in recall and F1 score compared to the previous state-of-the-art model on the corpus. The proposed algorithms can boost automation of literature screening during SLR in the clinical domain.
    Keywords:  Clinical literature screening; Intervention information detection; Natural language processing; Systematic review
    DOI:  https://doi.org/10.1016/j.jbi.2022.104185
  3. Comput Intell Neurosci. 2022 ;2022 4543467
    Application of Digital Information Technology in Book Classification and Quick Search in University Libraries.
    Huijie Xiao.
      A digital library is a digital information resource system supported by modern high technology, a next-generation information resource management model on the Internet, and the result of the digitization of library collections, and with the development of society and the accelerated pace of people's lives, people cannot spend too much time classifying and finding books, so the study of book classification and quick finding in university libraries is very important. This paper mainly researches and analyzes the classification and quick search of books in the university library through the algorithms and methods of digital information technology and finds a better algorithm. This paper mainly conducts experiments on automatic text and support vector machine (one-to-many and global optimization) methods and compares the obtained experimental data, such as classification accuracy, classification time, search time, and other data. The experimental results show that the classification accuracy of these three classification methods is in the range of 86%-94%. However, compared with the two methods of automatic text classification and one-to-many classification, the global optimization classification has the highest accuracy in the sample size of each interval. Among them, the classification time is the lowest for automatic text classification, which is less than 30s, and the one-to-many classification sample takes the most time, and their average fitness is in the range of 24%-27%.
    DOI:  https://doi.org/10.1155/2022/4543467
  4. J Med Internet Res. 2022 Feb 11.
    Predicting Emerging Themes in Rapidly Expanding COVID-19 Literature with Unsupervised Word Embeddings and Machine Learning.
    Ridam Pal, Harshita Chopra, Raghav Awasthi, Harsh Bandhey, Aditya Nagori, Tavpritesh Sethi.
       BACKGROUND: Evidence from peer-reviewed literature is the cornerstone for designing responses to global threats such as COVID-19. In the massive and rapidly growing corpuses such as the COVID-19 publications, assimilating and synthesizing information is challenging. Leveraging a robust computational pipeline that evaluates multiple aspects such as network topological features, communities and their temporal trends can make this process more efficient.
    OBJECTIVE: We aim to show that new knowledge can be captured and tracked using the temporal change in the underlying unsupervised word embeddings of literature. Further imminent themes can be predicted using machine learning upon the evolving associations between words.
    METHODS: Frequently occurring medical entities were extracted from the abstracts of more than 150,000 COVID-19 articles published on the WHO database, collected on a monthly interval starting from February 2020. Word embeddings trained on each month's literature were used to construct networks of entities with cosine similarities as edge weights. Topological features of the subsequent month's network were forecasted based on prior patterns and new links were predicted using supervised machine learning. Community detection and alluvial diagrams were used to track biomedical themes that evolved over the months.
    RESULTS: We found that thromboembolic complications were detected as an emerging theme as early as August 2020. A shift towards symptoms of Long COVID complications was observed during March 2021 and neurological complications gained significance in June 2021. A prospective validation of the link prediction models achieved an AUROC score of 0.87. Predictive modeling revealed predisposing conditions, symptoms, cross-infection and neurological complications as a dominant research theme in COVID-19 publications based on patterns observed in previous months.
    CONCLUSIONS: Machine learning-based prediction of emerging links can contribute towards steering research by capturing themes represented by groups of medical entities, based on patterns of semantic relationships over time.
    CLINICALTRIAL:
    DOI:  https://doi.org/10.2196/34067
  5. Database (Oxford). 2022 Aug 31. pii: baac069. [Epub ahead of print]2022
    Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations.
    Qingyu Chen, Alexis Allot, Robert Leaman, Rezarta Islamaj, Jingcheng Du, Li Fang, Kai Wang, Shuo Xu, Yuefu Zhang, Parsa Bagherzadeh, Sabine Bergler, Aakash Bhatnagar, Nidhir Bhavsar, Yung-Chun Chang, Sheng-Jie Lin, Wentai Tang, Hongtong Zhang, Ilija Tavchioski, Senja Pollak, Shubo Tian, Jinfeng Zhang, Yulia Otmakhova, Antonio Jimeno Yepes, Hang Dong, Honghan Wu, Richard Dufour, Yanis Labrak, Niladri Chatterjee, Kushagri Tandon, Fréjus A A Laleye, Loïc Rakotoson, Emmanuele Chersoni, Jinghang Gu, Annemarie Friedrich, Subhash Chandra Pujari, Mariia Chizhikova, Naveen Sivadasan, Saipradeep Vg, Zhiyong Lu.
      The coronavirus disease 2019 (COVID-19) pandemic has been severely impacting global society since December 2019. The related findings such as vaccine and drug development have been reported in biomedical literature-at a rate of about 10 000 articles on COVID-19 per month. Such rapid growth significantly challenges manual curation and interpretation. For instance, LitCovid is a literature database of COVID-19-related articles in PubMed, which has accumulated more than 200 000 articles with millions of accesses each month by users worldwide. One primary curation task is to assign up to eight topics (e.g. Diagnosis and Treatment) to the articles in LitCovid. The annotated topics have been widely used for navigating the COVID literature, rapidly locating articles of interest and other downstream studies. However, annotating the topics has been the bottleneck of manual curation. Despite the continuing advances in biomedical text-mining methods, few have been dedicated to topic annotations in COVID-19 literature. To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature. The BioCreative LitCovid dataset-consisting of over 30 000 articles with manually reviewed topics-was created for training and testing. It is one of the largest multi-label classification datasets in biomedical scientific literature. Nineteen teams worldwide participated and made 80 submissions in total. Most teams used hybrid systems based on transformers. The highest performing submissions achieved 0.8875, 0.9181 and 0.9394 for macro-F1-score, micro-F1-score and instance-based F1-score, respectively. Notably, these scores are substantially higher (e.g. 12%, higher for macro F1-score) than the corresponding scores of the state-of-art multi-label classification method. The level of participation and results demonstrate a successful track and help close the gap between dataset curation and method development. The dataset is publicly available via https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/ for benchmarking and further development. Database URL https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/.
    DOI:  https://doi.org/10.1093/database/baac069
  6. J Taibah Univ Med Sci. 2022 Oct;17(5): 844-852
    Reading habits of medical practitioners: Young doctors in Pakistan, a case study.
    Muhammad Ali Raza, Faizah Mohamad Nor, Rashid Mehmood.
       Objective: This study attempted to investigate the reading preferences and habits among young Pakistani medical doctors. The reading time, preferred source of information, preferred medical journals, and ways of reading medical journals were explored.
    Methods: A survey approach was used for data collection. The study participants were young medical professionals in Pakistan. An online survey was sent to more than 300 individuals through various physicians and their professional groups/bodies. A total of 155 responded to the questionnaire, and 128 of the questionnaires were considered worthy of data analysis.
    Results: Among respondents, 40% read printed journals, 49% read online journals, 60% read case reports, and 55% read newspapers for 1-5 h per week. Continuing medical education was the preferred source of information, and the Pakistan Journal of Cardiology & Thoracic Surgery was the preferred medical journal. Reading the abstract and the conclusion was the preferred way of reading journal articles.
    Conclusion: Young physicians are enthusiastic in participating in research activities and spending time gaining updated information. Physicians read articles methodically. Online sources of information are preferred over printed sources.
    Keywords:  Online reading; Pakistani physicians; Print reading; Reading habits; Reading medical journals; Reading preferences
    DOI:  https://doi.org/10.1016/j.jtumed.2022.04.007
  7. Comput Intell Neurosci. 2022 ;2022 3392489
    Research on Literature Clustering Algorithm for Massive Scientific and Technical Literature Query Service.
    Chen Zhang.
      Traditional science and technology literature search mainly provides users with reliable and detailed information materials and services through technical means, data resources, and service strategies. With the development of network technology, computer technology, and information technology, digital information resources are increasing day by day, which continuously impact the traditional knowledge service mode. Some traditional technical methods and service means can no longer meet the information needs of users under large data sets. This paper proposes a model of large-scale literature search service in the context of big data by studying the technical means and service modes used for scientific and technical literature search in universities in the era of big data. Specifically, this paper proposes a method for fast literature retrieval by combining R-tree indexing for the characteristics of diverse data types and large data volume of science and technology literature. The method uses an improved k-mean clustering algorithm to construct an R-tree clustering model and improve the retrieval efficiency of the system by retrieving scientific and technical literature data through R-tree indexing. Experiments on university science and technology literature datasets show that the method in this paper improves both efficiency and precision when searching literature.
    DOI:  https://doi.org/10.1155/2022/3392489
  8. J Med Internet Res. 2022 Sep 02. 24(9): e29609
    Laypeople's Online Health Information Search Strategies and Use for Health-Related Problems: Cross-sectional Survey.
    Yen-Lin Chiu, Chin-Chung Tsai, Jyh-Chong Liang.
       BACKGROUND: With the increase in the use of the internet to search for health information about health-related problems, there is a need for health care professionals to better understand how their patients search for and use the online health information that may influence their medical decision making.
    OBJECTIVE: The aims of this study are to explore laypeople's online health information search strategies and examine the relationships between their search strategies and utilization behavior of online health information.
    METHODS: Two scales, namely match and elaboration, were used to measure patients' basic search strategies (ie, simple approach) and advanced search strategies (ie, integrative approach), respectively. In addition, the consultation scale was used to evaluate the participants' use of online health information to consult doctors and others. A total of 253 outpatients without university education were purposely selected and surveyed. The participants were outpatients at a university-affiliated teaching hospital. Partial least squares-structural equation modeling (PLS-SEM) was performed to analyze the measurement model to specify the measurement validation. In addition, the structure model of PLS-SEM was evaluated to examine the path correlations between variables and to execute interaction effect and curvilinear relationship analyses.
    RESULTS: The results of the path correlation analysis by PLS-SEM showed that both elaboration strategy (path coefficient=0.55, P<.001) and match strategy (path coefficient=0.36, P<.001) were positively correlated with consultation on online health information with doctors and others. In addition, interaction effect and curvilinear relationship analyses indicated that there was a significant interaction effect between elaboration and match on consultation (path coefficient=-0.34, P<.001) and a significant curvilinear relationship between match and consultation (path coefficient=-0.09, P=.046).
    CONCLUSIONS: Increasing patients' exposure to online health information through both a simple search approach (ie, match strategy) and a complex search approach (ie, elaboration strategy) may lead them to appropriately use the information to consult doctors and others. However, the results of interaction effect and curvilinear relationship analyses highlighted the essential role of the elaboration strategy to properly locate, evaluate, and apply online health information. The findings of this study may help health care professionals better understand how to communicate with their patients through the health information on the internet.
    Keywords:  decision making; eHealth literacy; information search strategy; information-seeking behavior; internet; laypeople; online health information; patient; patient communication
    DOI:  https://doi.org/10.2196/29609
  9. Arthrosc Sports Med Rehabil. 2022 Aug;4(4): e1575-e1579
    Patient Education Materials Found via Google Search for Shoulder Arthroscopy Are Written at Too-High of a Reading Level.
    Youssef Abdullah, Aaron Alokozai, Abraham J Mathew, Michaela A Stamm, Mary K Mulcahey.
       Purpose: To evaluate the quality and correlation of readability on actionability and understandability of shoulder arthroscopy-related patient education materials (PEMs) found via a routine Google search.
    Methods: Two independent authors performed an online Google search with the term "shoulder arthroscopy." The first 5 pages of search results were then screened for PEMs. Journal articles, news articles, nontext materials, and unrelated websites were excluded. The readability of included resources was calculated using objective metrics: Flesch-Kincaid Grade Score, Simple Measure of Gobbledygook index, Coleman-Liau Index, and the Gunning Fog Index. Patient Education Material Assessment Tool for Printed Materials assessed for understandability and actionability. Associations between readability and actionability and understandability were determined using Spearman correlation and linear regression.
    Results: The searches returned 53 websites related to shoulder arthroscopy. A total of 34 (64%) met inclusion criteria. A high school reading level or greater was required to read the average PEM according to all scales used. The average PEM received a Patient Education Material Assessment Tool for Printed Materials score of 61.33 in understandability (range 18.75-89.47) and 55.59 points in actionability (range 16.67-83.33). An easily understood or actionable article would score at least 70 points. A moderate correlation was observed between readability and actionability on three of the scales used (r = 0.5, r = 0.59, r = 0.61).
    Conclusions: Most shoulder arthroscopy PEMs identified on Google are not written at a level that the average patient can read, understand, or act on (actionability).
    Clinical Relevance: Orthopaedic surgeons should be aware of the resources that patients use to obtain medical information. More accessible PEMs should be developed for patients undergoing shoulder arthroscopy to enhance comprehension of their condition and improve shared decision-making.
    DOI:  https://doi.org/10.1016/j.asmr.2022.04.034
  10. Cureus. 2022 Jul;14(7): e27406
    Assessing the Quality of YouTube Videos on Adhesive Capsulitis.
    Kevin Tang, Umair Azhar, Mustufa Babar, Atif Ahmed, Aaron Oh, Wesley Day, Hussein Harb, Ferdinand J Chan.
      Introduction  YouTube is the most popular video-based source of information on the Internet. It is accessed by over 1 billion users, which approximates to almost one-third of all Internet users. Orthopaedic video content published on YouTube is not screened and does not go through an editorial process, and most videos do not have information about authorship or appropriate references. Users who do not have the knowledge to assess the accuracy and reliability of the source may be misinformed about their medical condition. Previous studies have evaluated the quality of YouTube content for information in orthopaedics such as meniscus,kyphosis, and anterior cruciate ligament (ACL), but the quality of frozen shoulder videos on YouTube has not been investigated. The purpose of this study is to evaluate the quality and educational value of YouTube videos concerning adhesive capsulitis. Methods A YouTube search was performed using the term "frozen shoulder." Videos were excluded if they had no audio, were in a language other than English, or were longer than 10 minutes. A total of 70 videos were screened, and the first 50 videos that met the inclusion criteria were evaluated by three observers. Six video characteristics were extracted, and videos were categorized by source and content. Quality and educational value were assessed using the DISCERN (score range, 0-5), Global Quality score (GQS; score range, 0-4), and a Frozen Shoulder-Specific Score (FSSS; score range, 0-16). Results  The mean video duration was 242.46 ± 164.32 seconds. The mean number of views was 137,494 ± 262,756 and the total view count across 50 videos was 6,874,706. The mean DISCERN, GQS, and FSSS scores were 2.72 ± 0.85, 2.37 ± 0.895, and 4.42 ± 3.15, respectively. The video sources were primarily from non-physician healthcare professionals (32%), and most of the video content was focused on disease-specific information (50%). Significant between-group effects were observed for the DISCERN score and video source (P = .005), with videos from academic sources having the highest mean DISCERN score. DISCERN scores also differed significantly based on video content (P = .007), with disease content having the highest DISCERN score. Both GQS and FSSS scores differed significantly based on video content (both P < .001) but did not differ significantly based on the video source. Conclusions Information about frozen shoulder on YouTube is low quality and has limited educational value. Thus, providers for orthopaedic conditions should warn their patients and provide better alternatives for education.
    Keywords:  adhesive capsulitis; frozen shoulder; patient education; quality; social media; videos; youtube
    DOI:  https://doi.org/10.7759/cureus.27406
  11. Int Ophthalmol. 2022 Sep 03.
    Educational quality and content of YouTube videos on diabetic macular edema.
    Jasmine Mahajan, Aretha Zhu, Owais M Aftab, Roger K Henry, Nathan Y B Agi, Neelakshi Bhagat.
       PURPOSE: Diabetic macular edema (DME) is a vision-threatening complication of diabetes mellitus due to increased vascular permeability. Patients are increasingly using YouTube videos to educate themselves about DME. This study analyzes the content and quality of YouTube videos about DME.
    METHODS: Videos were searched in December 2021 for "diabetic macular edema." The first 100 videos sorted by both relevance and view count were reviewed (n = 200). Quantitative metrics and content were collected. Two reviewers assessed videos using the JAMA (0-4), modified DISCERN (1-5), and Global Quality Scale (GQS, 1-5). Videos were sorted into author groups: 1 (academic institutions/organizations), 2 (private practices/organizations), and 3 (independent users; ophthalmologist users noted). Statistical analyses were deemed significant at a = 0.05.
    RESULTS: One hundred four videos were included after applying exclusion criteria. Overall mean + standard deviations were 2.25 ± 0.83 (JAMA), 3.47 ± 0.55 (DISCERN), and 3.95 ± 0.95 (GQS). 51.9% of videos stated a definition, 32.7% mentioned screening, and 50% mentioned any DME risk factor. Healthcare professional-targeted videos had higher JAMA and DISCERN scores than patient-targeted videos (p < 0.05). Videos using ophthalmologists had higher JAMA and DISCERN scores than those lacking their presence (p < 0.05). JAMA scores significantly varied between author groups; within group 3, ophthalmologist-authored videos had higher DISCERN scores (p < 0.05).
    CONCLUSION: Videos without ophthalmologists or targeted toward patients had poor quality and content coverage. The rising prevalence of diabetes, coupled with increased internet use for acquiring medical information, creates a strong need for high-quality information about DME.
    Keywords:  Diabetes; Diabetic macular edema; Patient education; Retina; Social media; YouTube
    DOI:  https://doi.org/10.1007/s10792-022-02504-1
  12. Ther Adv Vaccines Immunother. 2022 ;10 25151355221118812
    Improving health literacy with mumps, measles and rubella (MMR) vaccination: comparison of the readability of MMR patient-facing literature and MMR scientific abstracts.
    Tina Downey, Beverley C Millar, John E Moore.
       Background: Historically, there have been many factors that have influenced mumps, measles and rubella (MMR) vaccine uptake, including media bias, social/economic determinants, parental education level, deprivation and concerns over vaccine safety. Readability metrics through online tools are now emerging as a means for healthcare professionals to determine the readability of patient-facing vaccine information. The aim of this study was to examine the readability of patient-facing materials describing MMR vaccination, through employment of nine readability and text parameter metrics, and to compare these with MMR vaccination literature for healthcare professionals and scientific abstracts relating to MMR vaccination.
    Materials and methods: The subscription-based online Readable program (readable.com) was used to determine nine readability indices using various readability formulae: Established readability metrics (n = 5) (Flesch-Kinkaid Grade Level, Gunning Fog Index, SMOG Index, Flesch Reading Ease and New Dale-Chall Score), as well as Text parameters (n = 4) (sentence count, word count, number of words per sentence, number of syllables per word) with 47 MMR vaccination texts [patient-facing literature (n = 22); healthcare professional-focused literature (n = 8); scientific abstracts (n = 17)].
    Results: Patient-facing vaccination literature had a Flesch Reading Ease score of 58.4 and a Flesch-Kincaid Grade Level of 8.1, in comparison with poorer readability scores for healthcare professional literature of 30.7 and 12.6, respectively. MMR scientific abstracts had the poorest readability (24.0 and 14.8, respectively). Sentence structure was also considered, where better readability metrics were correlated with significantly lower number of words per sentence and less syllables per word.
    Conclusion: Use of these readability tools enables the author to ensure their research is more readable to the lay audience. Patient co-production initiatives would help to ensure that not only can the target audience read the literature, but that they understand the content. Increased patient-centric focus groups would give better insights into reasons for MMR-associated vaccine hesitation and vaccine refusal.
    Keywords:  MMR; hesitancy; measles and rubella; mumps; readability; vaccination; vaccine uptake
    DOI:  https://doi.org/10.1177/25151355221118812
  13. Headache. 2022 Sep 03.
    Cluster headache - The worst possible pain on YouTube.
    Basit Ali Chaudhry, Thien Phu Do, Håkan Ashina, Messoud Ashina, Faisal Mohammad Amin.
      In clinical practice, patients with cluster headache often ask questions or mention information that they have seen or heard on the Internet. Because YouTube (www.youtube.com) is the second most visited Web site worldwide and offers a plethora of video content, we found it timely to ascertain the quality of information on cluster headache that is freely available on YouTube. We conducted an inquiry on YouTube on January 24, 2022, with the search term "cluster headache." Eligible YouTube videos included those with ≥10,000 views and content related to cluster headache. We assessed the quality and reliability of the videos with the Global Quality Scale and DISCERN, respectively. The search strategy identified 644 videos of which 134 were eligible for inclusion. The sources of the included videos were categorized as "healthcare professional/institution" (n = 45), "personal experience" (n = 52), and "other" (n = 37). According to the Global Quality Scale, 70 (52%) were low quality, 34 (25%) were of moderate quality, and 30 (22%) were of high quality. According to DISCERN, 104 (78%) were of low reliability, 28 (21%) were of moderate reliability, and 2 (1%) were of high reliability. The quality and reliability of cluster headache-related information on YouTube has room for improvement, even the content provided by healthcare providers. These findings should incentivize stakeholders, for example, government services, professional societies, healthcare providers, to provide accessible and better information on cluster headache.
    Keywords:  consumer health information; digital; education; online; patient perspective; social media
    DOI:  https://doi.org/10.1111/head.14368
  14. Health Info Libr J. 2022 Aug 31.
    Infodemiology for oral health and disease: A scoping review.
    Radhika Gupta, Vikrant Mohanty, Aswini Y Balappanavar, Puneet Chahar, Kavita Rijhwani, Sonal Bhatia.
       INTRODUCTION: Increasing affordability, accessibility and penetration of internet services worldwide, have substantially changed the ways of gathering health-related information. This has led to the origin of concept infodemiology that allows the information to be collected and analysed in near real time. Globally, oral diseases affect nearly 3.5 billion people; thus, volume and profile of oral health searches would help in understanding specific community dental needs and formulation of pertinent oral health strategies.
    AIM: To review the published literature on infodemiological aspects of oral health and disease.
    METHODOLOGY: This scoping review was conducted in accordance with PRISMA-ScR guidelines. Electronic search engines (Google Scholar) and databases (PubMed, Web of science, Scopus) were searched from 2002 onwards.
    RESULTS: Thirty-eight articles were included in this review. The infodemiological studies for oral health and disease were mainly used in two domains. Out of 38 articles, 24 accessed the quality of available online information and 15 studied online oral health-related information seeking behaviour.
    CONCLUSION: The most commonly searched oral diseases were toothache, oral cancer, dental caries, periodontal disease, oral maxillofacial surgical procedures and paediatric oral diseases. Most of the studies belonged to developed countries and Google was the most researched search engine.
    Keywords:  dental informatics; infodemiology; internet; oral health; social media
    DOI:  https://doi.org/10.1111/hir.12453
  15. J Intensive Care Soc. 2022 Aug;23(3): 340-344
    Assessing on-line medical education resources: A primer for acute care medical professionals and others.
    Peter G Brindley, Leon Byker, Simon Carley, Brent Thoma.
      The internet is increasingly used to propagate medical education, debate, and even disinformation. Therefore, this primer aims to help acute care medical professionals, as well as the public. This is because we all need to be able to critically appraise digital products, appraise content producers, and reflect upon our own on-line presence. This article discusses the challenges and opportunities associated with online medical resources. We then review Free Open Access Medical Education (FOAMed) and the key tools used to assess the trustworthiness of on-line medical products. Specifically, after discussing the pros and cons of traditional academic quality metrics, we compare and contrast the Social Media Index, the ALiEM AIR score, the Revised METRIQ Score, and gestalt. We also discuss internet search engines, peer review, and the important message behind the seemingly tongue-in-cheek Kardashian Index. Hopefully, this primer bolsters basic digital literacy and helps trainees, practitioners, and the public locate useful and reliable on-line resources. Importantly, we highlight the continued importance of traditional academic medicine and primary source publications.
    Keywords:  FOAMed; Medical education; medical publication; quality
    DOI:  https://doi.org/10.1177/1751143721999949
  16. J Public Health Afr. 2022 Jul 26. 13(2): 2011
    Perceived accessibility, and adequacy of COVID-19 related information in Nigeria.
    Chinenye Ifeoma Ubah, Linda Odikpo, Lovelyn Ndubuisi-Okoroezi, Chisom Mbadugha, Jennifer Ikechukwu-Okoroezi.
      Information on COVID-19 has evolved and blended with fake news, which the public, unfortunately, has to make an individual decision on how to use. As a result, access to authentic and adequate health information on COVID-19 is crucial for curbing the ongoing pandemic. The study was aimed at identifying sources of information on COVID-19 commonly used by adult Nigerian residents; determine the adequacy of information received; determine the accessibility of information on COVID-19 among Nigerians, and explore the relationship between location and access to information. An adapted version of the World Health Organization's (WHO) COVID-19 behavioral insight questionnaire was used to collect data from 1,039 adult residents in Nigeria across the geopolitical zones through an online survey. Analysis was done using SPSS version 24. Logistic regression was used to examine if location predicts access to information. Social media was identified as the major source of information among Nigerians. The top three accessible sources included social media 807(77.7%), television 546 (52.6%), and WHO websites 340 (32.7%). It was also found that they perceived information received on COVID-19 as adequate. The logistic regression model of the location did not predict access to COVID-19 information (p<0.05; 95% CI). Health authorities like the WHO, the ministry of health, CDC should optimize social media for better health information coverage.
    Keywords:  COVID-19; Nigeria; accessibility; adequacy; information
    DOI:  https://doi.org/10.4081/jphia.2022.2011
  17. Curr Pharm Teach Learn. 2022 Aug;pii: S1877-1297(22)00180-0. [Epub ahead of print]14(8): 1079
    COVID-19 health information sources and vaccine hesitancy: Correspondence.
    Pathum Sookaromdee, Viroj Wiwanitkit.
      
    Keywords:  COVID-19; Health; Hesitancy; Information; Source; Vaccine
    DOI:  https://doi.org/10.1016/j.cptl.2022.07.021
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