Journal of Nursing最新文献

{"title":"[From Sensing to Decision: Leveraging Technology to Enhance Nursing Clinical Judgment].","authors":"Yen-Chin Chen, Chun-Yin Yeh","doi":"10.6224/JN.26403","DOIUrl":"https://doi.org/10.6224/JN.26403","url":null,"abstract":"<p><p>With the rapid integration of Artificial Intelligence (AI), the Internet of Things, and sensing technologies into healthcare, the nursing frontline is undergoing a structural transformation from \"sensory-driven\" to \"data-driven\" practice. Tools such as wearable sensors, generative AI, and remote monitoring systems enable the real-time and continuous acquisition of physiological data. However, the increase in data volume does not inherently equate to improved clinical judgment. When excessive alerts and information infiltrate the workflow, these data may trigger cognitive overload and alarm fatigue. In this article, the \"Sensing-Integration-Decision\" framework is adopted to explore how sensing technologies expand the clinical observation capabilities of nurses and how clinical decision support systems facilitate the conversion of fragmented data into actionable care insights. Also, the essential competencies required of nurses in tech-assisted environments, including data literacy, clinical integration skills, and the core spirit of humanistic care, are explored. Furthermore, ethical issues arising from technology-mediated care such as data privacy, algorithmic bias, and the risk of over-reliance, are addressed. Finally, concrete recommendations for nursing education and practice are provided to guide professional development in the era of smart healthcare.</p>","PeriodicalId":35672,"journal":{"name":"Journal of Nursing","volume":"73 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148631872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Reducing the Incidence of Medical Adhesive-Related Skin Injuries in Premature Infants in the Neonatal Intensive Care Unit].","authors":"Yu-Ting Huang, Hung-Ya Liao, Tzu-Hsuan Chien, Jing-Yi Jhang, Ming-Ying Lee, Li-Chen Chen","doi":"10.6224/JN.26410","DOIUrl":"https://doi.org/10.6224/JN.26410","url":null,"abstract":"<p><strong>Background & problems: </strong>During the stay of premature infants in the neonatal intensive care unit, their important tubes must be fixed to the skin with medical adhesive products to ensure they remain securely emplaced. However, premature infants have immature skin development and are particularly prone to medical adhesive-related skin injuries (MARSI), with related incidence rates reaching as high as 47.4% in our unit.</p><p><strong>Purpose: </strong>The purpose of this project was to improve nurses' handling of medical adhesive products and the accuracy of their knowledge regarding MARSI in premature infants with the goal of reducing the incidence of MARSI in this patient population.</p><p><strong>Resolutions: </strong>A care guide manual and step-by-step diagram for using medical adhesive products were produced to provide nurses with a reference during the care process. Also, skin protection products were prepositioned to facilitate their correct usage, educational training was held, and a specialist assessment mechanism was established to improve the care capabilities and care quality of nurses.</p><p><strong>Results: </strong>One month after the project was launched, the accuracy rate in using medical adhesive products increased to 98.4%, the accuracy rate in recognizing MARSI in premature infants increased to 93.6%, and the incidence of MARSI in premature infants decreased from 47.4% to 2.9%.</p><p><strong>Conclusions: </strong>This project effectively improved clinical operations, facilitated the completion of standard operating procedures, enhanced nursing capabilities and the quality of care provided to premature infants, and, as a result, ensured the provision to premature infants of more complete and safer care.</p>","PeriodicalId":35672,"journal":{"name":"Journal of Nursing","volume":"73 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148631785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Care Transformation: The AI- and Data-Driven Shift From Reactive to Proactive and Predictive Care].","authors":"Hsiang-Chu Pai","doi":"10.6224/JN.26405","DOIUrl":"https://doi.org/10.6224/JN.26405","url":null,"abstract":"<p><p>With global population aging and the increasing prevalence of chronic diseases, the traditional reactive medical model, which focuses on post-illness management, is increasingly inadequate for addressing the structural challenges currently facing healthcare systems. With Taiwan now an aging society, the long-term care needs arising from disability, dementia, and multiple comorbidities further underscore the urgent need to transform existing models of care. This article begins by examining the necessity of care transformation driven by artificial intelligence (AI) and data, and then examines the emergence of \"proactive care\" and \"predictive care\" and related trends and empirical research. This article also examines how the nursing profession may evolve away from its reactive role and into a key participant in risk identification, health management, and predictive decision-making to strengthen person-centered continuity of care. Finally, the authors propose that a goal of future nursing education and system design should be to enhance data literacy, ethical governance, and interdisciplinary collaboration concurrently to promote the sustainable development of predictive care within Taiwan's healthcare system.</p>","PeriodicalId":35672,"journal":{"name":"Journal of Nursing","volume":"73 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148631797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[The Reliability and Validity Examination of the Development Support Competency Scale for Nurses: Traditional Chinese Version (DSCSN-TC)].","authors":"Chun-Chi Huang, Chung-Ying Lin, Mei-Chih Huang","doi":"10.6224/JN.26408","DOIUrl":"https://doi.org/10.6224/JN.26408","url":null,"abstract":"<p><strong>Background: </strong>The primary purpose of developmental care, the core requirement of premature infant care, is to provide a demand-central care model that reduces pressure in the environment to assist preterm infants in neonatal intensive care units (NICUs). While developmental care is the care aim of NICUs, no validated measures are currently available in Taiwan to evaluate the developmental care ability of nurses. To improve the quality of care provided to preterm infants and strengthen the on-the-job education received by nurses, the current status of relevant competencies in NICU nurses must be elucidated.</p><p><strong>Purpose: </strong>In this study, the original English version of the Developmental Support Competency Scale for Nurses (DSCS-N) developed by the Korean scholars Kim and Shin was translated into a Traditional Chinese version (DSCSN-TC) and evaluated for reliability and validity.</p><p><strong>Methods: </strong>The DSCS-N was translated using the Brislin translation model and cross-cultural equivalence was reviewed using the Jones process. Content validity (including content relevance and item clarity) was subsequently verified. Reliability and validity were assessed using descriptive statistics, confirmatory factor analysis, item analysis, and internal consistency reliability.</p><p><strong>Results: </strong>Convenience sampling was used to enroll 153 nurses (N = 153) as participants, all of whom had worked for more than one year in a NICU at one of three medical centers. The 19-item DSCSN-TC has a total possible score range of 19 to 76. Content Validity Index, evaluated by eight experts, was .92. The mean total score was 64.91 (SD = 7.61). CFA confirmed a good fit for the six-factor structure: CFI = .935, TLI = .919, RMSEA = .063, and SRMR = .05. The Cronbach's alpha values for the six factors were: interaction (.717), critical thinking (.833), parental support (.676), professional development (.762), partnership (.784), and environmental support (.688). The overall Cronbach's alpha for the scale was .935.</p><p><strong>Conclusions: </strong>The findings show the DSCSN-TC to be a reliable and valid measure when applied in either research or clinical settings to evaluate developmental care competency in nurses. Based on the preliminary testing results, the original instrument was further refined and evolved into a DSCSN-TC 2.0 version with the aim of providing an even more precise assessment of the developmental supportive care competence of nursing staff.</p>","PeriodicalId":35672,"journal":{"name":"Journal of Nursing","volume":"73 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148631856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[The Impact of Taiwan's Three-Shift Nurse-to-Patient Ratio Policy on Nursing Manpower in Hospitals at all Levels and Corresponding Countermeasures: An Observational Study].","authors":"Tsae-Jyy Wang, Sue-Hsien Chen, Wei-Fang Wang, Yea-Pyng Lin, Shu-Hua Hsieh, Hsiu-Ling Chou","doi":"10.6224/JN.26406","DOIUrl":"10.6224/JN.26406","url":null,"abstract":"<p><strong>Background: </strong>To establish a reasonable nurse-to-patient ratio across the daily three-shift nursing work schedule, the Ministry of Health and Welfare implemented nurse-to-patient ratio standards for all three shifts on March 1, 2024. The impact of this new standard on the nursing workforce at all three hospital levels warrants further investigation.</p><p><strong>Purpose: </strong>This study was designed to explore: 1. The impact of the implementation of this policy on the nursing manpower shortage in hospitals at each level; 2. The views of nursing supervisors and nursing staff on policy implementation.</p><p><strong>Methods: </strong>A descriptive research design was employed and nursing supervisors and nursing staff were recruited from hospitals at all levels. All data were collected using an online questionnaire.</p><p><strong>Results: </strong>Data from 207 nursing supervisors and 680 nursing staff were analyzed. The number of beds actually open in hospitals accounted for 84.7% (SD = 18.5) of the approved number. The average nursing vacancy rates in the first, second, and third quarter were 12.1%, 12.3%, and 11.7%, respectively. The average vacancy rate for nursing staff in regional hospitals was found to be significantly higher than those for the other two hospital levels. The actual average nursing manpower level was 86.8% (SD = 22.4) of the minimum level required to support the three-shift nurse-to-patient ratio. The average number of patients cared for by nursing staff in all three shifts was found to be significantly higher than the standard-defined nurse-to-patient ratio for these shifts. Nursing supervisors and staff members both agreed that salary adjustments, a friendly work environment, retention bonuses, third-shift allowances, vacancy allowances, and guaranteed vacation days were the primary factors affecting the achievement of the new three-shift nurse-to-patient ratio standard.</p><p><strong>Conclusions / implications for practice: </strong>The successful implementation of this policy requires taking into account the needs of hospitals of all levels, strengthening resource allocation, improving policy flexibility, optimizing human resource scheduling, and establishing a dynamic monitoring mechanism to ensure effective standards implementation and policy feasibility.</p>","PeriodicalId":35672,"journal":{"name":"Journal of Nursing","volume":"73 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148631726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Technology-Enabled Nursing Practice: Clinical Decision Support, Early Warning Analytics, and Human-Centered Care].","authors":"Hsiu-Chin Chen","doi":"10.6224/JN.26402","DOIUrl":"https://doi.org/10.6224/JN.26402","url":null,"abstract":"<p><p>This article was written to investigate technology-enabled nursing practice, with particular attention given to the integrated use of clinical decision support systems, artificial intelligence (AI)-driven early warning analytics, and human-centered care. As digital health technologies, electronic health records, remote monitoring, and AI continue to evolve rapidly, nursing practice has progressively shifted away from its former singular reliance on experiential judgment toward care models that incorporate data analytics, risk prediction, and real-time decision support. Evidence indicates that clinical decision support systems can enhance consistency in care processes, improve nurse recognition of patient deterioration, and support prioritization and interprofessional communication. Likewise, AI-driven early warning systems can leverage high-dimensional clinical data to detect patient deterioration earlier, thereby improving patient safety and quality of care. However, the integration of these technologies may also introduce challenges such as alert fatigue, workflow misalignment, reduced professional autonomy, and the erosion of the relational dimensions of care. Therefore, technology should be regarded not merely as an auxiliary tool but as a foundational element of contemporary nursing practice. Its design and implementation must align with nursing workflows, reinforce clinical reasoning, address ethical governance, and preserve patient dignity and human-centered values. Future efforts should prioritize nurse-led technology evaluations, education and training, and organizational governance to optimize patient outcomes and support professional development in increasingly technology-integrated nursing environments.</p>","PeriodicalId":35672,"journal":{"name":"Journal of Nursing","volume":"73 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148631695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Applying Developmental Care Theory to the Improvement of Self-Regulation and Feeding Difficulties in a Late Preterm Infant Receiving Cardiac Surgery: A Nursing Experience].","authors":"Yu-Tzu Lin, Hsiao-Ling Yang","doi":"10.6224/JN.26413","DOIUrl":"10.6224/JN.26413","url":null,"abstract":"<p><p>When preterm infants present with organ anomalies that require immediate surgical intervention after birth, their immature nervous systems often limit their ability to cope with the stress of surgery and subsequent treatments. This often results in difficulties with self-regulation and related health issues. The case described in this study involves a late preterm female infant born at 36+4 weeks of gestation who underwent cardiac surgery on the fifth day after birth. Postoperatively, she exhibited neonatal neurodevelopmental disorganization, ineffective suck-swallow reflexes, and inadequate nutritional intake. The first author provided nursing care between April 22 to May 10, 2025, with a telephone follow-up on May 14. The provided interventions were guided by Als' Synactive Theory of Development and integrated the Seven Neuroprotective Core Measures of the Neonatal Integrative Developmental Care Model, encompassing environmental regulation, sleep and positioning support, stress and pain management, and family-centered care. Objective evaluation was conducted using the Preterm Infant Oral Feeding Readiness Assessment Scale (POFRAS). The results demonstrated improvements in both self-regulation and feeding difficulties. Notably, the incidence of crying episodes decreased, the infant was able to sustain a calm state for longer periods, coordination of suck-swallow-breathing improved, and oral intake increased. Consequently, the amount of supplemental gavage feeding was reduced, caregivers were able to provide effective care, and the POFRAS score rose from 21 to 32. The comprehensive care approach described in this study integrates theoretical frameworks with behavioral cue-based observations to effectively improve feeding difficulties and self-regulation in preterm infants undergoing invasive surgery while simultaneously enhancing caregiver competence and confidence.</p>","PeriodicalId":35672,"journal":{"name":"Journal of Nursing","volume":"73 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148631864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[The Paradox and Integration of Digital Therapeutics: The Perspectives of Nursing Care and Care Ethics].","authors":"Yu-Hsiu Lin","doi":"10.6224/JN.26404","DOIUrl":"10.6224/JN.26404","url":null,"abstract":"<p><p>Digital therapeutics (DTx) are evidence-based, software-driven interventions that are expanding rapidly worldwide. However, current research has largely marginalized the nursing perspective, restricting the theoretical framework of DTx to clinical interventions rather than the fundamental caring relationship. This literature review, grounded in the core tenets of nursing care and ethics, was developed to synthesize five paradoxes inherent to DTx. These include: relational caring versus digital mediation; clinical judgment versus algorithmic decision-making; care ethics versus data compliance; workload relief versus digital administrative burden; and professional autonomy versus algorithmic governance. To address these tensions, a comprehensive four-stage nursing engagement framework is proposed that spans the entire DTx lifecycle of design, implementation, evaluation, and ethical governance. Furthermore, \"integration\" is reconceptualized as a dual-layered construct, highlighting the indispensable role of nurses in the clinical deployment of DTx. Finally, strategic recommendations are provided covering the dimensions of practice, education, research, and policy with the goal of actualizing a future of technology-empowered professionalism and humanistic care.</p>","PeriodicalId":35672,"journal":{"name":"Journal of Nursing","volume":"73 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148631818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Performance Comparison of Cognitive Screening Tools in Community-Dwelling Older Adults: The Correlation and Screening Performances of CogMate™ and MoCA].","authors":"Hui-Chu Chuang, Yi-Wen Chiu","doi":"10.6224/JN.26407","DOIUrl":"https://doi.org/10.6224/JN.26407","url":null,"abstract":"<p><strong>Background: </strong>In line with the current rapid pace of population aging in Taiwan, the population of individuals living with dementia has risen significantly. However, the early identification of dementia onset in community settings remains insufficient. Although the Montreal Cognitive Assessment (MoCA) has demonstrated good reliability and validity, its applicability in large-scale community screening settings is limited by administration time and manpower requirements. Therefore, more-accessible screening tools for dementia are urgently needed.</p><p><strong>Purpose: </strong>This study was designed to examine the feasibility of applying the game-based intelligent cognitive assessment CogMate™ on older adults in community settings and to evaluate the correlation and comparative discriminative validity of this tool with the MoCA.</p><p><strong>Methods: </strong>A cross-sectional study design with convenience sampling was employed. Two hundred and fifty community-dwelling adults aged 65 years and older in Taichung City were recruited as participants. The participants completed a demographic questionnaire, the MoCA, and CogMate™. The collected data were analyzed using descriptive statistics, Pearson correlation coefficients, intraclass correlation coefficients, and receiver operating characteristic (ROC) curve analysis.</p><p><strong>Results: </strong>After adjusting for age and educational level, based on a cutoff score of 23, 53.6% of the participants were classified as having suspected cognitive impairment based on MoCA total score. The MoCA total score was found to correlate significantly and positively with the CogMate™ total score (r = .612, p < .001), with an intraclass correlation coefficient of .601. The CogMate™ total score demonstrated good discriminative performance for detecting abnormal MoCA results, with an area under the receiver operating characteristic curve (AUC) of .813.</p><p><strong>Conclusions / implications for practice: </strong>CogMate™ demonstrated levels of consistency and validity similar to the MoCA, supporting its potential use as an early cognitive screening and referral tool in community settings. CogMate™ may serve as a complementary or alternative tool to the MoCA in community-based cognitive assessment.</p>","PeriodicalId":35672,"journal":{"name":"Journal of Nursing","volume":"73 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148631397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Technology-Driven, Human-Centered Care: Challenges and Opportunities].","authors":"Hsiang-Chu Pai","doi":"10.6224/JN.26401","DOIUrl":"https://doi.org/10.6224/JN.26401","url":null,"abstract":"<p><p>Rising costs, power shortages, and gaps in service accessibility and care quality rank among the most pressing challenges currently facing the global healthcare system. While artificial intelligence (AI)-based tools are being increasingly used in continuous patient monitoring to facilitate the timely identification of changes in patient condition and the delivery of proactive healthcare, AI applications face challenges related to data privacy, algorithmic bias, model interpretability, and ethics (Fahim et al., 2025). This issue's feature, \"The Technology-Driven Paradigm Shift in Nursing: Ethics, Professional Responsibility, and Competency Transformation,\" shares four articles with readers. The first focuses on clinical decision support systems, AI-driven early-warning analysis, and human-centered care integration. In the second, the authors explore how sensing technologies may be used to expand the clinical observation capabilities of nurses as well as the related care ethics implications. The final two articles discuss proactive and predictive care initiation and trends, proposing future nursing education and system designs should simultaneously strengthen data literacy, ethical governance, and interdisciplinary collaboration and providing new insights and directions for technology-facilitated nursing professional competence development. The human-centered approach at the heart of the nursing profession emphasizes the needs, feelings, and interactive experiences of individuals. Human-centered care not only focuses on the patient but also values the holistic, systemic relationship among family members, healthcare professionals, technology, and the care environment. The aim of the participatory and systematic-design framework underpinning this care approach is to improve healthcare service quality. While technology can make nursing faster and more convenient, healthcare professionals remain critical to imbuing the nursing process with meaning, empathy, and interpersonal connections. Therefore, the core value of \"technology-driven\" should be enhancing care capabilities and quality rather than replacing professionals. The emphasis of AI development in the realm of healthcare must be on user participation, trust building, ethical safety, and human-machine collaboration to clearly demonstrate the importance and future opportunities of human-centered care empowered by technology (Bevilacqua et al., 2025; Sadeghigolafshani et al., 2026).</p>","PeriodicalId":35672,"journal":{"name":"Journal of Nursing","volume":"73 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148631779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}