\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"9218",leadTitle:null,fullTitle:"Bayesian Inference on Complicated Data",title:"Bayesian Inference on Complicated Data",subtitle:null,reviewType:"peer-reviewed",abstract:"Due to great applications in various fields, such as social science, biomedicine, genomics, and signal processing, and the improvement of computing ability, Bayesian inference has made substantial developments for analyzing complicated data. This book introduces key ideas of Bayesian sampling methods, Bayesian estimation, and selection of the prior. It is structured around topics on the impact of the choice of the prior on Bayesian statistics, some advances on Bayesian sampling methods, and Bayesian inference for complicated data including breast cancer data, cloud-based healthcare data, gene network data, and longitudinal data. This volume is designed for statisticians, engineers, doctors, and machine learning researchers.",isbn:"978-1-83880-386-5",printIsbn:"978-1-83880-385-8",pdfIsbn:"978-1-83962-704-0",doi:"10.5772/intechopen.83214",price:119,priceEur:129,priceUsd:155,slug:"bayesian-inference-on-complicated-data",numberOfPages:118,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"5cf83c23db5b0ae47192d34ec8091162",bookSignature:"Niansheng Tang",publishedDate:"July 15th 2020",coverURL:"https://cdn.intechopen.com/books/images_new/9218.jpg",numberOfDownloads:5190,numberOfWosCitations:0,numberOfCrossrefCitations:6,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:5,numberOfDimensionsCitationsByBook:1,hasAltmetrics:0,numberOfTotalCitations:11,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 16th 2019",dateEndSecondStepPublish:"May 31st 2019",dateEndThirdStepPublish:"July 30th 2019",dateEndFourthStepPublish:"October 18th 2019",dateEndFifthStepPublish:"December 17th 2019",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"221831",title:"Prof.",name:"Niansheng",middleName:null,surname:"Tang",slug:"niansheng-tang",fullName:"Niansheng Tang",profilePictureURL:"https://mts.intechopen.com/storage/users/221831/images/system/221831.jpeg",biography:"Niansheng Tang is a Professor of Statistics and Dean of the School of Mathematics and Statistics, Yunnan University, China. He was elected a Yangtze River Scholars Distinguished Professor in 2013, a member of the International Statistical Institute (ISI) in 2016, a member of the board of the International Chinese Statistical Association (ICSA) in 2018, and a fellow of the Institute of Mathematical Statistics (IMS) in 2021. He received the ICSA Outstanding Service Award in 2018 and the National Science Foundation for Distinguished Young Scholars of China in 2012. He serves as a member of the editorial board of Statistics and Its Interface and Journal of Systems Science and Complexity. He is also a field editor for Communications in Mathematics and Statistics. His research interests include biostatistics, empirical likelihood, missing data analysis, variable selection, high-dimensional data analysis, Bayesian statistics, and data science. He has published more than 190 research papers and authored five books.",institutionString:"Yunnan University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Yunnan University",institutionURL:null,country:{name:"China"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"977",title:"Mathematical Modeling",slug:"mathematics-statistics-mathematical-modeling"}],chapters:[{id:"69160",title:"On the Impact of the Choice of the Prior in Bayesian Statistics",doi:"10.5772/intechopen.88994",slug:"on-the-impact-of-the-choice-of-the-prior-in-bayesian-statistics",totalDownloads:812,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:1,abstract:"A key question in Bayesian analysis is the effect of the prior on the posterior, and how we can measure this effect. Will the posterior distributions derived with distinct priors become very similar if more and more data are gathered? It has been proved formally that, under certain regularity conditions, the impact of the prior is waning as the sample size increases. From a practical viewpoint it is more important to know what happens at finite sample size n. In this chapter, we shall explain how we tackle this crucial question from an innovative approach. To this end, we shall review some notions from probability theory such as the Wasserstein distance and the popular Stein’s method, and explain how we use these a priori unrelated concepts in order to measure the impact of priors. Examples will illustrate our findings, including conjugate priors and the Jeffreys prior.",signatures:"Fatemeh Ghaderinezhad and Christophe Ley",downloadPdfUrl:"/chapter/pdf-download/69160",previewPdfUrl:"/chapter/pdf-preview/69160",authors:[null],corrections:null},{id:"71603",title:"A Brief Tour of Bayesian Sampling Methods",doi:"10.5772/intechopen.91451",slug:"a-brief-tour-of-bayesian-sampling-methods",totalDownloads:1007,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Unlike in the past, the modern Bayesian analyst has many options for approximating intractable posterior distributions. This chapter briefly summarizes the class of posterior sampling methods known as Markov chain Monte Carlo, a type of dependent sampling strategy. Varieties of algorithms exist for constructing chains, and we review some of them here. Such methods are quite flexible and are now used routinely, even for relatively complicated statistical models. In addition, extensions of the algorithms have been developed for various goals. General-purpose software is currently also available to automate the construction of samplers, freeing the analyst to focus on model formulation and inference.",signatures:"Michelle Y. Wang and Trevor Park",downloadPdfUrl:"/chapter/pdf-download/71603",previewPdfUrl:"/chapter/pdf-preview/71603",authors:[null],corrections:null},{id:"70031",title:"A Review on the Exact Monte Carlo Simulation",doi:"10.5772/intechopen.88619",slug:"a-review-on-the-exact-monte-carlo-simulation",totalDownloads:653,totalCrossrefCites:1,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Perfect Monte Carlo sampling refers to sampling random realizations exactly from the target distributions (without any statistical error). Although many different methods have been developed and various applications have been implemented in the area of perfect Monte Carlo sampling, it is mostly referred by researchers to coupling from the past (CFTP) which can correct the statistical errors for the Monte Carlo samples generated by Markov chain Monte Carlo (MCMC) algorithms. This paper provides a brief review on the recent developments and applications in CFTP and other perfect Monte Carlo sampling methods.",signatures:"Hongsheng Dai",downloadPdfUrl:"/chapter/pdf-download/70031",previewPdfUrl:"/chapter/pdf-preview/70031",authors:[null],corrections:null},{id:"69837",title:"Bayesian Analysis for Random Effects Models",doi:"10.5772/intechopen.88822",slug:"bayesian-analysis-for-random-effects-models",totalDownloads:590,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Random effects models have been widely used to analyze correlated data sets, and Bayesian techniques have emerged as a powerful tool to fit the models. However, there has been scarce literature that systematically reviews and summarizes the recent advances of Bayesian analyses of random effects models. This chapter reviews the use of the Dirichlet process mixture (DPM) prior to approximate the distribution of random errors within the general semiparametric random effects models with parametric random effects for longitudinal data setting and failure time setting separately. In a survival setting with clusters, we propose a new class of nonparametric random effects models which is motivated from the accelerated failure models. We employ a beta process prior to tact clustering and estimation simultaneously. We analyze a new data set integrated from Alzheimer’s disease (AD) study to illustrate the presented model and methods.",signatures:"Junshan Shen and Catherine C. Liu",downloadPdfUrl:"/chapter/pdf-download/69837",previewPdfUrl:"/chapter/pdf-preview/69837",authors:[null],corrections:null},{id:"68821",title:"Bayesian Inference of Gene Regulatory Network",doi:"10.5772/intechopen.88799",slug:"bayesian-inference-of-gene-regulatory-network",totalDownloads:812,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Gene regulatory networks (GRN) have been studied by computational scientists and biologists over 20 years to gain a fine map of gene functions. With large-scale genomic and epigenetic data generated under diverse cells, tissues, and diseases, the integrative analysis of multi-omics data plays a key role in identifying casual genes in human disease development. Bayesian inference (or integration) has been successfully applied to inferring GRNs. Learning a posterior distribution than making a single-value prediction of model parameter makes Bayesian inference a more robust approach to identify GRN from noisy biomedical observations. Moreover, given multi-omics data as input and a large number of model parameters to estimate, the automatic preference of Bayesian inference for simple models that sufficiently explain data without unnecessary complexity ensures fast convergence to reliable results. In this chapter, we introduced GRN modeling using hierarchical Bayesian network and then used Gibbs sampling to identify network variables. We applied this model to breast cancer data and identified genes relevant to breast cancer recurrence. In the end, we discussed the potential of Bayesian inference as well as Bayesian deep learning for large-scale and complex GRN inference.",signatures:"Xi Chen and Jianhua Xuan",downloadPdfUrl:"/chapter/pdf-download/68821",previewPdfUrl:"/chapter/pdf-preview/68821",authors:[null],corrections:null},{id:"72164",title:"Patient Bayesian Inference: Cloud-Based Healthcare Data Analysis Using Constraint-Based Adaptive Boost Algorithm",doi:"10.5772/intechopen.91171",slug:"patient-bayesian-inference-cloud-based-healthcare-data-analysis-using-constraint-based-adaptive-boos",totalDownloads:554,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Cloud-based healthcare data are a form of distributed data over the internet. The internet has become the most vulnerable part of critical healthcare infrastructures. Healthcare data are considered to be sensitive information, which can reveal a lot about a patient. For healthcare data, apart from confidentiality, privacy and protection of data are very sensitive issues. Proactive measures such as early warning are required to reduce the risk of patient’s data violation. This chapter investigates the ability of Patient Bayesian Inference (PBI) for network scenario analysis with violation of patient data to produce early warning. The Bayesian inference allows modeling the uncertainties that come with the problem of dealing with missing data, allows integrating data from remote nodes, and explicitly indicates dependence and independence. The use of constraint-based adaptive boost algorithm can demonstrate the patient’s Bayesian inference performance in the real-world datasets from healthcare data.",signatures:"Shahid Naseem",downloadPdfUrl:"/chapter/pdf-download/72164",previewPdfUrl:"/chapter/pdf-preview/72164",authors:[null],corrections:null},{id:"68575",title:"The Bayesian Posterior Estimators under Six Loss Functions for Unrestricted and Restricted Parameter Spaces",doi:"10.5772/intechopen.88587",slug:"the-bayesian-posterior-estimators-under-six-loss-functions-for-unrestricted-and-restricted-parameter",totalDownloads:762,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In this chapter, we have investigated six loss functions. In particular, the squared error loss function and the weighted squared error loss function that penalize overestimation and underestimation equally are recommended for the unrestricted parameter space −∞∞; Stein’s loss function and the power-power loss function, which penalize gross overestimation and gross underestimation equally, are recommended for the positive restricted parameter space 0∞; the power-log loss function and Zhang’s loss function, which penalize gross overestimation and gross underestimation equally, are recommended for 01. Among the six Bayesian estimators that minimize the corresponding posterior expected losses (PELs), there exist three strings of inequalities. However, a string of inequalities among the six smallest PELs does not exist. Moreover, we summarize three hierarchical models where the unknown parameter of interest belongs to 0∞, that is, the hierarchical normal and inverse gamma model, the hierarchical Poisson and gamma model, and the hierarchical normal and normal-inverse-gamma model. In addition, we summarize two hierarchical models where the unknown parameter of interest belongs to 01, that is, the beta-binomial model and the beta-negative binomial model. 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In many cases, models of given inverse problems can be linearized which allows the use of methods of linear algebra for their solutions. Effective tools of linear algebra in linear inverse problems are, in particular, generalized inverse matrices as one of the ways to represent (pseudo)solutions to singular (differential) matrix equations. Nowadays, the theory of generalized inverses is one of the hot topics of linear algebra in various aspects, such as elements of the ring, operators of Hilbert space, or matrices with real, complex, and quaternion entries. Matrices over quaternion algebra are also useful tools in a lot of applied inverse problems, among them in signal and color image processing, quantum physics, etc. In recent years, methods of simultaneous decompositions for tensors have been actively used in different inverse problems. In particular, a product singular value decomposition of a quaternion tensor triplet (higher-order PSVD) has various applications in digital watermarking technology. The main goals of this book are both to give the last achievements in various areas of linear algebra, such as generalized inverses and their applications in solving matrix equations and matrix minimization problems, decompositions of matrices and tensors, new developments in theories of quaternion matrices, and operators of Hilbert space, etc. It is also important to consider new applying models of inverse problems that can be linearized.
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In 2008, he held a Doctor of Philosophy (Candidate of Science) degree from Taras Shevchenko National University of Kyiv in specialty of Algebra and the Theory of Numbers. His PhD thesis "Theory of the column and row determinants and inverse matrix over a skew field with involution" introduces and develops the theory of new column and row determinants for matrices with noncommutative entries. In 2021, he was awarded a Doctor of Physical and Mathematical Sciences degree from Institute of Mathematics of NAS of Ukraine in Kyiv. His habilitation ScD thesis " Generalized inverse matrices over the quaternion skew field and their applications" is devoted to generalized inverse matrices over the quaternion skew field, first of all to their determinantal representations, and their applications to solving quaternion matrix equations, some differential matrix equations, and problems of quaternion matrix minimizations and approximations. 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These changes present new challenges to health systems and require them to adapt to the new and dynamic era of the 21st century.
Macro-level changes and consequent challenges include the digital revolution [1, 2]; the need to work with, and alongside, technological innovations and artificial intelligence (AI) [3]; frequent reforms and changes to regulations (citation removed for blinding); an increase in life expectancy and other demographic changes; and the need to address all of these changes with limited resources and in face of increasing competition within health care organizations [4].
Micro-level challenges include changes in patients’ consumerist approach and in patients’ access to information, as well as changes in the power relations between patients and care- givers, and the entry of younger generations (Generations Y and Z) into the workplace.
In order to address these challenges and effectively carry out these systemic changes and prepare healthcare students and professionals to additional current and future changes, healthcare personnel need to adopt a wide range of new skills, and healthcare systems need to find effective ways to disseminate these skills among present and future employees [5].
Faced with the need to change and modify healthcare systems, one crucial set of skills that has been predominantly referred to is “soft skills”. The term describes skills that are not strictly cognitive or technical [6] and ones that include both intrapersonal and interpersonal competencies [7]. Thus, many of the soft skills which are now referred to as “critical skills” or “core skills”, are included in the concept of Emotional Intelligence (EI).
In its essence, EI involves an optimal combination of emotion and thought and consists of one’s ability to identify, use, understand and manage feelings in oneself and in others [8, 9]. Several models have proposed a set of emotional and social skills and competencies that are related to emotionally intelligent behaviors and outcomes in various fields, and which can be actively developed [10, 11, 12]. Such skills typically include self-awareness, awareness of others (empathy), emotional management (self-regulation) and interpersonal communication.
In recent years, a broad body of research has highlighted the contribution of EI and social–emotional skills to areas such as physical and psychological health; interpersonal relationships; and effectiveness and success in academic studies and in a wide range of organizations, occupations and levels of employment [11, 13, 14, 15]. In particular, a large number of studies have emphasized the importance of SEI for coping with challenges of the 21st century [16].
In the field of medicine, social–emotional skills have been linked to success across a wide range of positions and roles [17]. Noted examples include links to effective performance under pressure, increased commitment to healthcare organizations, positive interpersonal communications, and effective teamwork among medical staff [18]. Additional studies have pointed out correlations between SEI and better doctor–patient relations [19], fewer medical lawsuits [20], empathic treatment [21], precision in medical diagnoses and consequently in treatment [22], lower levels of situation-related anxiety in patients [23], higher levels of patient responsiveness to treatment, increased patient satisfaction and higher patient trust in healthcare staff [24]. Yet despite the clear benefits offered by social–emotional skills in the healthcare professions, the social–emotional skills of medical students have often been noted to be similar or even lower than those of the average population [25], and at times were even noted to decrease during their studies [26].
Consequently, there has been a call to develop social–emotional intelligence among medical students, nursing students and medical management students [27]. Nevertheless, to date, and despite a growing understanding of the importance of social–emotional skills to medical professions, the development of these skills has only captured a limited place in medical school curricula and in the training of healthcare staff.
Until recently, both admissions to healthcare education programs and subsequent academic success were defined primarily on the basis of superior cognitive abilities. Traditional teaching, learning and assessment processes focused on knowledge and on cognitive abilities (citation removed for blinding).
However, in recent years, it has been increasingly recognized that cognitive and professional abilities are not sufficient criteria for success in medical schools and in the medical profession. Consequently, several hospitals have begun incorporating social–emotional skill development efforts as part of ongoing training for their medical teams [28]. Several medical schools have also introduced admission measures that examine candidates’ personal and interpersonal abilities [29] as well as courses for social–emotional skill development [30]. Nevertheless, such efforts are still limited, due to time and overload constraints and to a lingering, mainly cognitive, focus. This paper will introduce the theoretical and methodological underpinning of a novel tool for the development of social–emotional skills, suitable for use among medical students and staff.
In order to integrate the development of social–emotional skills into existing academic and training curricula in a wide field of subjects, a unique Social–Emotional Skill Development tool (SE-SD) that is based on guidelines for effective practices in social–emotional development (e.g. [31, 32]) has been recently developed. This tool is highly suited for healthcare education systems, in particular in light of the above-noted time constraints and the challenges imposed by a predominantly cognitive focus [2, 33].
The SE-SD tool offers a broad perspective on the field of healthcare education and addresses the need for non-cognitive social–emotional skills in the field. In addition, it aims to complement and to work in synergy with existing training tools and to support other professional skills. It can be further viewed more broadly as a method and pedagogy for integrating the development of social–emotional skills in healthcare training. By doing so, it is expected to enable students to cope with a changing healthcare reality and thrive in it.
The SE-SD tool is based on eight underlying principles:
The theoretical framework at the basis of the SE-SD tool is the well-established Bar-On [11] model of Emotional–Social Intelligence. This framework, which addresses both behaviors and outcomes, has been noted to be especially suitable for educational settings [34, 35] and has been employed successfully in the medical arena [36, 37, 38]. It allows for a holistic and inclusive development approach that has been noted especially effective in SEI trainings [39]. Similar to the Bar-On framework and associated tool (EQ-i), the SE-SD tool includes ten skills, nine such as emotional self-awareness and expression, self-regulation, empathy and interpersonal relations, social responsibility, flexibility, stress-tolerance, optimism and self-regard from the original Bar-On model (Figure 1) [11], with the addition of Growth Mindset. All linked to different aspects of various healthcare professions [25]. The SE-SD skills are arranged in 4 major clusters: intrapersonal, inter-personal, adaptability and stress-management, to which the well-being indicator of the original model has been added.
List of essential soft skills for healthcare professions.
In line with the inclusive model at its basis and the wide variety of social–emotional skills it includes, the SE-SD tool addresses a offers a wide range of development assignments and a wide range of methodical tools (such as dedicated articles, video clips, short interviews, reflective questions, real-life experimentation, etc.). This variability is in line with earlier studies where the successful development of social–emotional skills was noted to include both cognitive and emotional components and to require varied and experiential methods [40].
It has been widely acknowledged that SEI development cannot be achieved by means of a single workshop [41, 42, 43, 44] and requires an extensive, routinised, long-term effort that provides time for learning, practicing and achieving development [31]. A curriculum integrated approach allows for such extensive and long-term development efforts as well as for offering a contextual, rather than isolated, experience.
In line with these findings, the SE-SD tool has been designed to be integrated into existing course materials. This integrated approach helps students and instructors overcome time constraints as well as highlights the links between the targeted SEI skills and different aspects of the profession, making the development relevant and meaningful. Integration is achieved through two parallel processes: by linking specific social–emotional skills with the general course material; and through home assignments that target SEI development, are self-paced and are completed and evaluated at different points in time throughout the entire course.
Social–emotional skills have been noted to be relevant across a wide range of roles and positions in the field of healthcare [17].
Accordingly, the SE-SD tool, and in particular the assignments associated with each skill, were structured in a more general manner and therefore can be easily applied to a variety of subjects in the academic curriculum and can be integrated into a variety of healthcare academic courses, disciplines and academic levels.
The SE-SD tool can be used in a modular and “spiral” manner in order to integrate a wide range of skills into different courses throughout the academic program. Such modularity is particularly suited to the development of social–emotional skills, a process noted to involve continuous and lifelong learning [45, 46] and in which links between skills exist [11]. Importantly, while the SE-SD tool can be used in isolation, as part of a single course or a number of courses, a multiyear, spiral SEI development program which corresponds with the desired graduate vision can enhance the overall effectiveness and sustainability of the SEI development process [31] and ultimately contribute to the quality of healthcare professionals.
The nature of healthcare studies supports such modularity as healthcare students may need to employ different SEI skills at different stages of their academic training (pre-clinical and clinical years for example).
Finally, assignments that form part of the SE-SD tool are constructed in a modular fashion, building up from theory to practice. This modular structure is built on the premise that theoretical knowledge provides a foundation for the development of SEI [44] and that effective social–emotional development should follow several steps: the acquisition of a theoretical basis; and an understanding of the concept of EI and the specific SEI skills as they are being targeted, achieved through theoretical assignments. These are followed by gaining understanding of the relevance of the targeted skills to the course material and to future practice through reflective assignments. Practical assignments then provide a step-by-step opportunity to develop and practice newly acquired skills. Ideally, these steps lead to changes in habits, attitudes and behaviors [31, 44].
The SE-SD tool focuses on the process of SEI development rather than on its outcomes. It is assumed that social–emotional development is an on-going life-long process which takes place within relationships (in this case, with the course instructor). It has been demonstrated that relationships that are based on trust, guidance, support and formative feedbacks enhance SEI development [12, 32]. As an inseparable part of the SE-SD tool, therefore, evaluations and feedbacks from course instructors provide formative comments which students can use in order to continue to progress and to refine their development process. All these elements have been noted to enhance SEI development [47] and motivation levels [31].
Academic assignments that integrate the SE-SD tool are evaluated based on their degree of completion and on the students’ level of understanding of several elements: the concept of EI, the specific SEI skills that are being acquired, and the relevance of these skills to the particular healthcare profession at the center of the course. During the active development stage, students are evaluated based on their level of engagement and reflection. The importance of reflective learning has been previously highlighted [44], noting that reflections on thoughts, feelings and behaviors that underlie attitudes and habits, both personal and of others, enhance the development of SEI competencies.
As emotions, thoughts, competencies, behaviors and habits are all unique to each individual, it is recommended that social–emotional development processes include a focus on individual social–emotional skills [12, 48]. To this end, the SE-SD tool allows students to follow their own individual development path at their own pace, to start the development process from their own individual starting point, to focus on their own specific goals and to self-assess their progress. Self-directed processes encourage participants to be personally accountable for their progress and involve them in planning, carrying out and evaluating their own learning experiences. These elements, in turn, were noted to enhance motivation, which is key to social–emotional training success [12].
In order to manage their self-directed learning, students receive “road maps” (either in a digital form or in print) that outline their assignments and set specific points in time for evaluations and feedbacks. The assignments are order-dependent, as each assignment builds on the previous one, and the order in which assignments are offered is pre-determined. While students can follow the development process at their own pace, they are instructed to avoid completing their assignments all at once. This time-paced approach maximizes the effectiveness of the learning process, allowing students time for reflection and practice and providing instructors with at least two opportunities, at two different points in time, to deliver evaluations and feedbacks to students.
The SE-SD tool is both flexible and adaptable. The tool’s flexibility is manifested in the choice of SEI skills that are to be associated with a given course, in the links drawn between these skills and the course materials, in the number of assignments chosen from the selection offered and in the variety of these assignments. The tool offers further flexibility in terms of feedbacks and evaluations: instructors can limit themselves to the two formal evaluations (intermediate and final) that are provided as part of the tool, but may choose to provide additional informal and more frequent feedbacks. Furthermore, additional skills to those offered in the model can be added, consistent with the principles embedded in the tool: designing developmental activities and assignments that would support knowledge acquisition and enhance the understanding of these skills, determining the relevance of the added skill to a particular course, and identifying starting points and goals for each of the participants.
Taken together, these features of the SE-SD allow healthcare education systems to include social–emotional development as a strategic plan for preparing students and workers for a changing professional reality.
Skill-development processes often follow a sequence of stages that have been recognized to contribute to effective development (e.g. [31, 41, 47]). These include: preparation (gaining students’ commitment, identifying needs, and jointly designing a development program); action (implementing the program – introduction and development); and evaluation. In the case of the SE-SD tool and healthcare education settings, these stages have been adapted to meet the specific requirements of various healthcare professions (Figure 2).
Process of model implementation.
The preparation stage begins with the selection of an SEI skill to be developed during a given course. This skill, selected by the course instructor from a list of social–emotional skills offered in the tool (Figure 1), is chosen based on its degree of relevance to the course material and its suitability in terms of the students’ academic level.
The course instructor then plans how to integrate the skill into the course material. For example, if integrated into a course that focuses on patient-caregiver relationships, the instructor may decide to discuss empathy as part of a segment that examines how to deliver difficult news to patients.
Lastly, the course instructor goes over the SE-SD assignment list and chooses assignments that correspond to the selected skill.
When a three-year process is involved, these steps are followed in group discussions where faculty members jointly decide on the skills to be introduced each year and the classes they most fit.
The action stage includes two parts, introduction and development.
This part of the action stage is designed to highlight the relevance and importance of SEI to the students and to promote motivation to participate in the EI development process. The course instructor begins by introducing the concept of social–emotional skills to the students, highlights the relevance of these skills to the course and notes their importance to students’ overall growth and future careers in the 21st century.
Following these introductory remarks, the SE-SD model and tool are presented and the methods by which the development process will be incorporated into the course, both during class and by means of home assignments and their evaluation, are explained. The instructor notes the order by which the assignments are to be completed, the corresponding time frames, and the formative evaluation method by which they would be assessed.
The development part of the action stage relies on individual home assignments that students are asked to carry out throughout the entire course. Students are instructed to complete the assignments in a pre-determined order and can only access subsequent assignments after completing the previous ones. In line with the structure of the SE-SD tool, the assignments include three hierarchical segments: Theoretical background, Exploration, and Practice.
The theoretical background segment aims to provide the students with a solid theoretical basis for personal development. Students learn about the concept of EI and come to understand the targeted SEI skills and the mechanisms by which they may be employed. As part of this segment, students are encouraged to read relevant literature and to watch illustrative video presentations. For example, as part of a background segment on empathy, students may read a paper about the concept, watch a relevant video program, note the distinction between empathy, sympathy and compassion, and find out more about the mechanisms by which empathy is employed and its contribution to the healthcare professions.
During the exploration segment, students proceed to explore the relevance of targeted EI skills to various healthcare professions. By answering a set of guiding questions and/or conducting short interviews with professional in the field, students are able to identify the relevance of any given skill to their course material, to their chosen profession and to present and future life outcomes. Finally, they are asked to identify their own individual starting point with respect to the targeted skill and to define the corresponding goals. The SE-SD tool provides a set of guiding questions and/or a short questionnaire that support this exploration process. For example, in the case of empathy, students are asked to identify links between empathy and healthcare professions, use guiding questions to evaluate the gains they are likely to derive from enhanced empathy, and assess their starting point with regards to the development process using an empathy questionnaire.
The practice segment involves students in a wide range of activities, all aimed at developing the targeted SEI skill. This is the longest segment of the three, in line with Boyatzis [12, 32] who noted the importance of experimenting with new behaviors for an effective social–emotional development process. The assignments that form the core of this segment are designed to develop cognitive, emotional and behavioral components of the targeted skill. For example, in the case of empathy, the students are asked to engage in empathic dialogues, take on another person’s perspective (e.g. a patient or a team member), or examine case studies that center on interactions between patients and caregivers. The culminating assignment includes reflection and self-evaluation.
Finally, during the evaluation segment, students are provided with feedbacks and formative assessments that can help them further develop their social–emotional skills. These feedbacks and evaluations are provided by their course instructors at two points in time during each course: mid-term (which coincides with the goal-setting stage of the development process) and at the end of the course (after all assignments have been completed). These feedbacks are accompanied by self-evaluations.
As noted above, participant students are evaluated based on their level of commitment, efforts and engagement in the development process (as opposed to the level of development that has been achieved); their level of understanding of the targeted EI skill; and the level of personal and professional reflectiveness they demonstrate throughout the entire course.
Furthermore, beyond the mid-term and end-of-term evaluations, instructors can choose to provide additional evaluations and feedbacks in the course of the program, in accordance with available time resources.
In light of the global changes and challenges that face many professionals in the 21st century, there is an increasing understanding that it is of primary importance to develop and foster social–emotional skills, also referred to as “soft skills”, among workers in a wide range of fields. A prominent example is the field of healthcare. Skills that are likely to benefit healthcare professionals may include emotional self-awareness, self-regulation, empathy, and interpersonal relations. These, as well as other social–emotional skills, were noted to improve coping abilities, academic learning and professional effectiveness among both medical teams and healthcare management teams [49, 50].
The importance of social–emotional skills to medical staff and to healthcare systems and their currently limited place in medical school curricula, call for a proactive initiative on the part of academic institutions. Such an initiative should address needs and challenges, both current and future, that face healthcare professionals and can transform medical schools from knowledge providers to leaders of cultural and social changes.
Given the noted difficulties to integrate the development of social–emotional skills into existing curricula in the field of healthcare, we propose a novel and holistic SE-SD tool that integrates social–emotional learning into existing curricula while overcoming time and workload barriers.
Furthermore, the assignments that form part of the SE-SD tool are prepared and provided ahead of time by the tool designers, and therefore instructors do not require any prior expertise in the field of social–emotional learning in order to implement the tool as part of their courses.
Effective implementation of social–emotional skill development programs has been noted to benefit from a supportive climate. It is therefore highly recommended that faculty is included in the proposed social–emotional training process. Social–emotional training is expected to heighten faculty awareness of the importance of the process, increase their willingness to take risks as they implement the SE-SD model in their respective institutions, and enable them to model socially-emotionally behaviors and to ‘walk the talk’ [49]. All these were found to contribute to the development of social–emotional skills in students.
In addition to academic institutions which can take upon themselves to develop social–emotional skills in students and faculty, development of these skills should form an integral part of on-going professional training for both healthcare staff in post-academic settings. Such life-long learning will support earlier development efforts in academic institutions and will insure its sustainability.
Lastly, although research regarding the effectiveness of the SE-SD tool is still a work in progress, we believe that the use of integrative learning methodologies like the one described here would bring healthcare academic institutions and their graduates one step closer towards adapting to the 21st century and meeting its demands.
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The objective of this review chapter is to critically evaluate and highlight the role of mitochondria in the development of DOX-induced cardiotoxicity.",book:{id:"6060",slug:"mitochondrial-diseases",title:"Mitochondrial Diseases",fullTitle:"Mitochondrial Diseases"},signatures:"Celal Guven, Yusuf Sevgiler and Eylem Taskin",authors:[{id:"192567",title:"Prof.",name:"Eylem",middleName:null,surname:"Taskin",slug:"eylem-taskin",fullName:"Eylem Taskin"},{id:"195229",title:"Dr.",name:"Celal",middleName:null,surname:"Guven",slug:"celal-guven",fullName:"Celal Guven"},{id:"206996",title:"Prof.",name:"Yusuf",middleName:null,surname:"Sevgiler",slug:"yusuf-sevgiler",fullName:"Yusuf Sevgiler"}]}],onlineFirstChaptersFilter:{topicId:"186",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"81348",title:"Mouse Models to Understand Mutagenic Outcomes and Illegitimate Repair of DNA Damage",slug:"mouse-models-to-understand-mutagenic-outcomes-and-illegitimate-repair-of-dna-damage",totalDownloads:14,totalDimensionsCites:0,doi:"10.5772/intechopen.103929",abstract:"Maintenance of genome integrity is critical to prevent cell death or disease. Illegitimate repair of chromosomal DNA breaks can lead to mutations and genome rearrangements which are a well-known hallmark of multiple cancers and disorders. Endogenous causes of DNA double-strand breaks (DSBs) include reactive oxygen species (ROS) and replication errors while exogenous causes of DNA breaks include ionizing radiation, UV radiation, alkylating agents, and inhibitors of topoisomerase II (Top2). Recent evidence suggests that a growing list of environmental agents or toxins and natural dietary compounds also cause DNA breaks. Understanding the consequences of exposure to a broad spectrum of DSB-inducing agents has significant implications for understanding mutagenicity, genome stability and human health. This chapter will review in vivo mouse models designed to measure DNA damage and mutagenicity, and illegitimate repair of DNA DSBs caused by exposure to environmental agents.",book:{id:"11348",title:"Mutagenesis and Mitochondrial-Associated Pathologies",coverURL:"https://cdn.intechopen.com/books/images_new/11348.jpg"},signatures:"Kiran Lalwani, Caroline French and Christine Richardson"},{id:"79374",title:"Mitochondrial Cytopathies of the Renal System",slug:"mitochondrial-cytopathies-of-the-renal-system",totalDownloads:73,totalDimensionsCites:0,doi:"10.5772/intechopen.96850",abstract:"Mitochondria are major intracellular organelles with a variety of critical roles like adenosine triphosphate production, metabolic modulation, generation of reactive oxygen species, maintenance of intracellular calcium homeostasis, and the regulation of apoptosis. Mitochondria often undergo transformation in both physiological and pathological conditions. New concepts point that mitochondrial shape and structure are intimately linked with their function in the kidneys and diseases related to mitochondrial dysfunction have been identified. Diseases associated with mitochondrial dysfunction are termed as “mitochondrial cytopathies”. Evidence support that there is a role of mitochondrial dysfunction in the pathogenesis of two common pathways of end-stage kidney disease, namely, chronic kidney disease (CKD) and acute kidney injury (AKI). Mitochondrial cytopathies in kidneys mainly manifest as focal segmental glomerular sclerosis, tubular defects, and as cystic kidney diseases. The defects implicated are mutations in mtDNA and nDNA. The proximal tubular cells are relatively vulnerable to oxidative stress and are therefore apt to suffer from respiratory chain defects and manifest as either loss of electrolyte or low-molecular-weight proteins. Patients with mitochondrial tubulopathy are usually accompanied by myoclonic epilepsy and ragged red muscle fibers (MERRF), and Pearson’s, Kearns-Sayre, and Leigh syndromes. The majority of genetic mutations detected in these diseases are fragment deletions of mtDNA. Studies have shown significantly increased ROS production, upregulation of COX I and IV expressions, and inactivation of complex IV in peripheral blood mononuclear cells of patients with stage IV–V CKD, thereby demonstrating the close association between mitochondrial dysfunction and progression to CKD. Furthermore, the mechanisms that translate cellular cues and demands into mitochondrial remodeling and cellular damage, including the role of microRNAs and lncRNAs, are examined with the final goal of identifying mitochondrial targets to improve treatment of patients with chronic kidney diseases.",book:{id:"11348",title:"Mutagenesis and Mitochondrial-Associated Pathologies",coverURL:"https://cdn.intechopen.com/books/images_new/11348.jpg"},signatures:"Lovelesh K. Nigam, Aruna V. Vanikar, Rashmi Dalsukhbhai Patel, Kamal V. Kanodia, Kamlesh Suthar and Umang Thakkar"},{id:"75895",title:"Maneuvering Mitochondria for Better Understanding of Therapeutic Potential of mtDNA Mutation",slug:"maneuvering-mitochondria-for-better-understanding-of-therapeutic-potential-of-mtdna-mutation",totalDownloads:116,totalDimensionsCites:0,doi:"10.5772/intechopen.96915",abstract:"Heterogeneity of mitochondrial diseases in terms of genetic etiology and clinical management makes their diagnosis challenging. Mitochondrial genome, basic mitochondrial genetics, common mutations, and their correlation with human diseases is well-established now and advances in sequencing is accelerating the molecular diagnostics of mitochondrial diseases. Major research focus now is on development of mtDNA intervention techniques like mtDNA gene editing, transfer of exogenous genes (sometimes even entire mtDNA) that would compensate for mtDNA mutations responsible for mitochondrial dysfunction. Although these genetic manipulation techniques have good potential for treatment of mtDNA diseases, research on such mitochondrial manipulation fosters ethical issues. The present chapter starts with an introduction to the factors that influence the clinical features of mitochondrial diseases. Advancement in treatments for mitochondrial diseases are then discussed followed by a note on methods for preventing transmission of these diseases.",book:{id:"11348",title:"Mutagenesis and Mitochondrial-Associated Pathologies",coverURL:"https://cdn.intechopen.com/books/images_new/11348.jpg"},signatures:"Sanket Tembe"},{id:"75720",title:"Mitochondria and Eye",slug:"mitochondria-and-eye",totalDownloads:275,totalDimensionsCites:0,doi:"10.5772/intechopen.96368",abstract:"Mitochondria are essential subcellular organelles and important key regulators of metabolism. Mammalian mitochondria contain their own DNA (mtDNA). Human mtDNA is remarkably small (16,569 bp) compared to nuclear DNA. Mitochondria promote aerobic respiration, an important part of energy metabolism in eukaryotes, as the site of oxidative phosphorylation (OXPHOS). OXPHOS occurs in the inner membrane of the mitochondrion and involves 5 protein complexes that sequentially undergo reduction-oxygen reactions ultimately producing adenosine triphosphate (ATP). Tissues with high metabolic demand such as lungs, central nervous system, peripheral nerves, heart, adrenal glands, renal tubules and the retina are affected preferentially by this critical role in energy production by mitochondrial disorders. Eye-affected mitochondrial disorders are always primary, but the role of mitochondrial dysfunction is now best understood in acquired chronic progressive ocular diseases. Recent advances in mitochondrial research have improved our understanding of ocular disorders. In this chapter, we will discuss the mitochondria in relation to eye diseases, ocular tumors, pathogenesis, and treatment modalities that will help to improve the outcomes of these conditions.",book:{id:"11348",title:"Mutagenesis and Mitochondrial-Associated Pathologies",coverURL:"https://cdn.intechopen.com/books/images_new/11348.jpg"},signatures:"Lata Singh and Mithalesh Kumar Singh"}],onlineFirstChaptersTotal:4},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:98,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:288,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:10,numberOfPublishedChapters:103,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"25",title:"Environmental Sciences",doi:"10.5772/intechopen.100362",issn:"2754-6713",scope:"\r\n\tScientists have long researched to understand the environment and man’s place in it. The search for this knowledge grows in importance as rapid increases in population and economic development intensify humans’ stresses on ecosystems. Fortunately, rapid increases in multiple scientific areas are advancing our understanding of environmental sciences. 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