JMIR Medical Education最新文献

{"title":"Effectiveness of an Interactive Web-Based Clinical Practice Monitoring System on Enhancing Motivation in Clinical Learning Among Undergraduate Nursing Students: Longitudinal Quasi-Experimental Study in Tanzania.","authors":"Patricia Herman, Stephen M Kibusi, Walter C Millanzi","doi":"10.2196/45912","DOIUrl":"https://doi.org/10.2196/45912","url":null,"abstract":"<p><strong>Background: </strong>Nursing students' motivation in clinical learning is very important not only for their academic and professional achievement but also for making timely, informed, and appropriate decisions in providing quality and cost-effective care to people. However, the increased number of students and the scarcity of medical supplies, equipment, and patients, just to mention a few, have posed a challenge to educators in identifying and navigating the best approaches to motivate nursing students to learn during their clinical placements.</p><p><strong>Objective: </strong>This study primarily used descriptive and analytical methods to examine undergraduate nursing students' desire for clinical learning both before and after participating in the program.</p><p><strong>Methods: </strong>An uncontrolled longitudinal quasi-experimental study in a quantitative research approach was conducted from February to March 2021 among 589 undergraduate nursing students in Tanzania. Following a baseline evaluation, nursing students were enrolled in an interactive web-based clinical practice monitoring system by their program, institution, names, registration numbers, and emails via unique codes created by the lead investigator and trainers. The system recorded and generated feedback on attendance, clinical placement unit, selected or performed clinical nursing procedures, and in-between and end-of-shift feedback. The linear regression was used to assess the effect of the intervention (interactive web-based clinical practice monitoring system) controlled for other correlated factors on motivation in clinical learning (outcome) among nursing students. Nursing students' sociodemographic characteristics and levels of motivation in clinical learning were analyzed descriptively while a 2-tailed paired sample t test established a comparative mean difference in motivation in clinical learning between the pretest and the posttest. The association between variables was determined using regression analysis set at a 95% CI and 5% statistical significance.</p><p><strong>Results: </strong>The mean age of study participants (N=589) was 23 (SD 2.69) years of which 383 (65.0%) were male. The estimated effect (β) of a 3-week intervention to improve nursing students' motivation in clinical learning was 3.041 (P=.03, 95% CI 1.022-7.732) when controlled for other co-related factors. The mean score for motivation in clinical learning increased significantly from the baseline (mean 9.31, SD 2.315) to the postintervention (mean 20.87, SD 5.504), and this improvement presented a large effect size of 2.743 (P<.001, 95% CI 1.011-4.107).</p><p><strong>Conclusions: </strong>Findings suggest that an interactive web-based clinical practice monitoring system is viable and has the potential to improve undergraduate nursing students' motivation for clinical learning. One alternative clinical pedagogy that educators in nursing education can use to facilitate clinical learning ac","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e45912"},"PeriodicalIF":3.2,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12059505/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144050962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Datathons to Teach AI in Undergraduate Medical Education: Case Study.","authors":"Michael Steven Yao, Lawrence Huang, Emily Leventhal, Clara Sun, Steve J Stephen, Lathan Liou","doi":"10.2196/63602","DOIUrl":"10.2196/63602","url":null,"abstract":"<p><strong>Background: </strong>As artificial intelligence and machine learning become increasingly influential in clinical practice, it is critical for future physicians to understand how such novel technologies will impact the delivery of patient care.</p><p><strong>Objective: </strong>We describe 2 trainee-led, multi-institutional datathons as an effective means of teaching key data science and machine learning skills to medical trainees. We offer key insights on the practical implementation of such datathons and analyze experiences gained and lessons learned for future datathon initiatives.</p><p><strong>Methods: </strong>We detail 2 recent datathons organized by MDplus, a national trainee-led nonprofit organization. To assess the efficacy of the datathon as an educational experience, an opt-in postdatathon survey was sent to all registered participants. Survey responses were deidentified and anonymized before downstream analysis to assess the quality of datathon experiences and areas for future work.</p><p><strong>Results: </strong>Our digital datathons between 2023 and 2024 were attended by approximately 200 medical trainees across the United States. A diverse array of medical specialty interests was represented among participants, with 43% (21/49) of survey participants expressing an interest in internal medicine, 35% (17/49) in surgery, and 22% (11/49) in radiology. Participant skills in leveraging Python for analyzing medical datasets improved after the datathon, and survey respondents enjoyed participating in the datathon.</p><p><strong>Conclusions: </strong>The datathon proved to be an effective and cost-effective means of providing medical trainees the opportunity to collaborate on data-driven projects in health care. Participants agreed that datathons improved their ability to generate clinically meaningful insights from data. Our results suggest that datathons can serve as valuable and effective educational experiences for medical trainees to become better skilled in leveraging data science and artificial intelligence for patient care.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e63602"},"PeriodicalIF":3.2,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12017604/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144053376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Making Medical Education Courses Visible: Theory-Based Development of a National Database.","authors":"Andi Gashi, Monika Brodmann Maeder, Eva K Hennel","doi":"10.2196/62838","DOIUrl":"https://doi.org/10.2196/62838","url":null,"abstract":"<p><strong>Background: </strong>Medical education has undergone professionalization during the last decades, and internationally, educators are trained in specific medical education courses also known as \"train the trainer\" courses. As these courses have developed organically based on local needs, the lack of a general structure and terminology can confuse and hinder educators' information and development. The first aim of this study was to conduct a national search, analyze the findings, and provide a presentation of medical education courses based on international theoretical frameworks to support Swiss course providers and educators searching for courses. The second aim was to provide a blueprint for such a procedure to be used by the international audience.</p><p><strong>Objective: </strong>In this study, we devised a scholarly approach to sorting and presenting medical education courses to make their content accessible to medical educators. This approach is presented in detailed steps and our openly available exemplary database to make it serve as a blueprint for other settings.</p><p><strong>Methods: </strong>Following our constructivist paradigm, we examined content from medical education courses using a theory-informed inductive data approach. Switzerland served as an example, covering 4 languages and different approaches to medical education. Data were gathered through an online search and a nationwide survey with course providers. The acquired data and a concurrently developed keyword system to standardize course terminology are presented using Obsidian, a software that shows data networks.</p><p><strong>Results: </strong>Our iterative search included several strategies (web search, survey, provider enquiry, and snowballing) and yielded 69 courses in 4 languages, with varying terminology, target audiences, and providers. The database of courses is interactive and openly accessible. An open-access template database structure is also available.</p><p><strong>Conclusions: </strong>This study proposes a novel method for sorting and visualizing medical education courses and the competencies they cover to provide an easy-to-use database, helping medical educators' practical and scholarly development. Notably, our analysis identified a specific emphasis on undergraduate teaching settings, potentially indicating a gap in postgraduate educational offerings. This aspect could be pivotal for future curriculum development and resource allocation. Our method might guide other countries and health care professions, offering a straightforward means of cataloging and making information about medical education courses widely available and promotable.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e62838"},"PeriodicalIF":3.2,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12017612/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144001369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Online-Based and Technology-Assisted Psychiatric Education for Trainees: Scoping Review.","authors":"Mohd Amiruddin Mohd Kassim, Sidi Muhammad Yusoff Azli Shah, Jane Tze Yn Lim, Tuti Iryani Mohd Daud","doi":"10.2196/64773","DOIUrl":"10.2196/64773","url":null,"abstract":"<p><strong>Background: </strong>The concept of online learning in medical education has been gaining traction, but whether it can accommodate the complexity of higher-level psychiatric training remains uncertain.</p><p><strong>Objective: </strong>This review aims to identify the various online-based and technology-assisted educational methods used in psychiatric training and to examine the outcomes in terms of trainees' knowledge, skills, and levels of confidence or preference in using such technologies.</p><p><strong>Methods: </strong>A comprehensive search was conducted in PubMed, Cochrane, PsycINFO, Scopus, and ERIC to identify relevant literature from 1991 until 2024. Studies in English and those that had English translations were identified. Studies that incorporated or explored the use of online-based or technology-assisted learning as part of psychiatric training in trainees and had outcomes of interest related to changes in the level of knowledge or skills, changes in the level of preference or confidence in using online-based or technology-assisted learning, and feedback of participants were included. Studies were excluded if they were conducted on populations excluding psychiatric trainees or residents, were mainly descriptive of the concept of the intervention without any relevant study outcome, were not in English or did not have English translations, or were review articles.</p><p><strong>Results: </strong>A total of 82 articles were included in the review. The articles were divided into 3 phases: prior to 2015, 2015 to 2019 (prepandemic), and 2020 onward (postpandemic). Articles mainly originated from Western countries, and there was a significant increase in relevant studies after the pandemic. There were 5 methods identified, namely videoconference, online modules/e-learning, virtual patients, software/applications, and social media. These were applied in various aspects of psychiatric education, such as theory knowledge, skills training, psychotherapy supervision, and information retrieval.</p><p><strong>Conclusions: </strong>Videoconference-based learning was the most widely implemented approach, followed by online modules and virtual patients. Despite the outcome heterogeneity and small sample sizes in the included studies, the application of such approaches may have utility in terms of knowledge and skills attainment and could be beneficial for the training of future psychiatrists, especially those in underserved low- and middle-income countries.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e64773"},"PeriodicalIF":3.2,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12041828/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144017993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Authors' Reply: Enhancing AI-Driven Medical Translations: Considerations for Language Concordance.","authors":"Joyce Teng, Roberto Andres Novoa, Maria Alexandrovna Aleshin, Jenna Lester, Kira Seiger, Fiatsogbe Dzuali, Roxana Daneshjou","doi":"10.2196/71721","DOIUrl":"https://doi.org/10.2196/71721","url":null,"abstract":"","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e71721"},"PeriodicalIF":3.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12007921/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143989330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing AI-Driven Medical Translations: Considerations for Language Concordance.","authors":"Stephanie Quon, Sarah Zhou","doi":"10.2196/70420","DOIUrl":"https://doi.org/10.2196/70420","url":null,"abstract":"","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e70420"},"PeriodicalIF":3.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12039939/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144014093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Motivation Theories and Constructs in Experimental Studies of Online Instruction: Systematic Review and Directed Content Analysis.","authors":"Adam Gavarkovs, Erin Miller, Jaimie Coleman, Tharsiga Gunasegaran, Rashmi A Kusurkar, Kulamakan Kulasegaram, Melanie Anderson, Ryan Brydges","doi":"10.2196/64179","DOIUrl":"10.2196/64179","url":null,"abstract":"<p><strong>Background: </strong>The motivational design of online instruction is critical in influencing learners' motivation. Given the multifaceted and situated nature of motivation, educators need access to a range of evidence-based motivational design strategies that target different motivational constructs (eg, interest or confidence).</p><p><strong>Objective: </strong>This systematic review and directed content analysis aimed to catalog the motivational constructs targeted in experimental studies of online motivational design strategies in health professions education. Identifying which motivational constructs have been most frequently targeted by design strategies-and which remain under-studied-can offer valuable insights into potential areas for future research.</p><p><strong>Methods: </strong>Medline, Embase, Emcare, PsycINFO, ERIC, and Web of Science were searched from 1990 to August 2022. Studies were included if they compared online instructional design strategies intending to support a motivational construct (eg, interest) or motivation in general among learners in licensed health professions. Two team members independently screened and coded the studies, focusing on the motivational theories that researchers used and the motivational constructs targeted by their design strategies. Motivational constructs were coded into the following categories: intrinsic value beliefs, extrinsic value beliefs, competence and control beliefs, social connectedness, autonomy, and goals.</p><p><strong>Results: </strong>From 10,584 records, 46 studies were included. Half of the studies (n=23) tested strategies aimed at making instruction more interesting, enjoyable, and fun (n=23), while fewer studies tested strategies aimed at influencing extrinsic value beliefs (n=9), competence and control beliefs (n=6), social connectedness (n=4), or autonomy (n=2). A focus on intrinsic value beliefs was particularly evident in studies not informed by a theory of motivation.</p><p><strong>Conclusions: </strong>Most research in health professions education has focused on motivating learners by making online instruction more interesting, enjoyable, and fun. We recommend that future research expand this focus to include other motivational constructs, such as relevance, confidence, and autonomy. Investigating design strategies that influence these constructs would help generate a broader toolkit of strategies for educators to support learners' motivation in online settings.</p><p><strong>Trial registration: </strong>PROSPERO CRD42022359521; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022359521.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e64179"},"PeriodicalIF":3.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12032500/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144051816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large Language Models in Biochemistry Education: Comparative Evaluation of Performance.","authors":"Olena Bolgova, Inna Shypilova, Volodymyr Mavrych","doi":"10.2196/67244","DOIUrl":"10.2196/67244","url":null,"abstract":"<p><strong>Background: </strong>Recent advancements in artificial intelligence (AI), particularly in large language models (LLMs), have started a new era of innovation across various fields, with medicine at the forefront of this technological revolution. Many studies indicated that at the current level of development, LLMs can pass different board exams. However, the ability to answer specific subject-related questions requires validation.</p><p><strong>Objective: </strong>The objective of this study was to conduct a comprehensive analysis comparing the performance of advanced LLM chatbots-Claude (Anthropic), GPT-4 (OpenAI), Gemini (Google), and Copilot (Microsoft)-against the academic results of medical students in the medical biochemistry course.</p><p><strong>Methods: </strong>We used 200 USMLE (United States Medical Licensing Examination)-style multiple-choice questions (MCQs) selected from the course exam database. They encompassed various complexity levels and were distributed across 23 distinctive topics. The questions with tables and images were not included in the study. The results of 5 successive attempts by Claude 3.5 Sonnet, GPT-4-1106, Gemini 1.5 Flash, and Copilot to answer this questionnaire set were evaluated based on accuracy in August 2024. Statistica 13.5.0.17 (TIBCO Software Inc) was used to analyze the data's basic statistics. Considering the binary nature of the data, the chi-square test was used to compare results among the different chatbots, with a statistical significance level of P<.05.</p><p><strong>Results: </strong>On average, the selected chatbots correctly answered 81.1% (SD 12.8%) of the questions, surpassing the students' performance by 8.3% (P=.02). In this study, Claude showed the best performance in biochemistry MCQs, correctly answering 92.5% (185/200) of questions, followed by GPT-4 (170/200, 85%), Gemini (157/200, 78.5%), and Copilot (128/200, 64%). The chatbots demonstrated the best results in the following 4 topics: eicosanoids (mean 100%, SD 0%), bioenergetics and electron transport chain (mean 96.4%, SD 7.2%), hexose monophosphate pathway (mean 91.7%, SD 16.7%), and ketone bodies (mean 93.8%, SD 12.5%). The Pearson chi-square test indicated a statistically significant association between the answers of all 4 chatbots (P<.001 to P<.04).</p><p><strong>Conclusions: </strong>Our study suggests that different AI models may have unique strengths in specific medical fields, which could be leveraged for targeted support in biochemistry courses. This performance highlights the potential of AI in medical education and assessment.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e67244"},"PeriodicalIF":3.2,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12005600/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144054290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of the Inverted Classroom Approach in a Case-Study Course on Antithrombotic Drug Use in a PharmD Curriculum: French Monocentric Randomized Study.","authors":"Georges Jourdi, Mayssa Selmi, Pascale Gaussem, Jennifer Truchot, Isabelle Margaill, Virginie Siguret","doi":"10.2196/67419","DOIUrl":"https://doi.org/10.2196/67419","url":null,"abstract":"<p><strong>Background: </strong>Appropriate antithrombotic drug use is crucial knowledge for pharmacy students.</p><p><strong>Objective: </strong>We sought to compare the inverted classroom (IC) approach to a traditional question-and-answer educational approach with the aim of enhancing pharmacy students' engagement with a case-study course on antithrombotic drug use.</p><p><strong>Methods: </strong>Third-year PharmD (Doctor of Pharmacy) students from Paris Cité University were randomly assigned to control (n=171) and IC (n=175) groups. The latter were instructed to read and prepare the preprovided course material 1 week before the in-class session to assume the instructor role on the target day, whereas students of the control group attended a traditional case-study course carried out by the same instructor. All students completed pre- and posttest multiple-choice questions surveys assessing their knowledge levels as well as stress, empathy, and satisfaction questionnaires.</p><p><strong>Results: </strong>A significantly higher participation rate was observed in the control group (93/171, 54%) compared to the IC group (65/175, 37%; P=.002). Women (110/213, 52%) participated more than men (48/133, 36%; P=.002) whatever the group was. Students' knowledge scores from both groups had similar results with no difference neither in the prescore (1.17, SD 0.66 and 1.24, SD 0.72 of 5, respectively) nor in the short-term knowledge retention (2.45, SD 0.61 and 2.35, SD 0.73, respectively). The IC approach did not increase student stress or enhance their empathy for the instructor. It increased the preclass workload (P=.02) and was not well received among students.</p><p><strong>Conclusions: </strong>This study showed that the traditional educational approach remains an efficient method for case-study courses in the early stages (ie, third-year) of the 6-year PharmD curriculum, yet dynamic methods improving the active role of students in the learning process are still needed.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e67419"},"PeriodicalIF":3.2,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12039941/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144062725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Citation Accuracy Challenges Posed by Large Language Models.","authors":"Manlin Zhang, Tianyu Zhao","doi":"10.2196/72998","DOIUrl":"10.2196/72998","url":null,"abstract":"","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e72998"},"PeriodicalIF":3.2,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12037895/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143774438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}