Mental problems of COVID-19 nurses and prevention strategies.
\r\n\tPrevalence of reading disability among school-age children depends upon the criteria used for definition; however, the prevalence of written expression disorders in estimated to be between 5 and 12 percent, the prevalence of written expression disorders is estimated to be between 7 and 15 percent, while the prevalence of dyscalculia is estimated to be between 3 and 6 percent.
\r\n\r\n\tRisk factors for learning disorders are family history, socio-economic conditions, prematurity, presence of other developmental, mental and health conditions (e.g. behavioral disorders, autism, attention deficit and hyperactivity disorders), prenatal exposition to neurotoxic agents, genetic disorders, particular medical conditions, history of traumatic brain injury or other neurological conditions.
",isbn:"978-1-83968-588-0",printIsbn:"978-1-83968-587-3",pdfIsbn:"978-1-83968-589-7",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,hash:"0999e5f759c2380ae5a4a2ee0835c98d",bookSignature:" Sandro Misciagna",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10910.jpg",keywords:"Learning Disability Definition, Brain Plasticity, Learning Disability Evaluation, Learning Disabilities Resources, Psychoeducation Evaluation, Clinical Features, Dyslexia, Dysgraphia, Dyscalculia, Intellectual Disabilities, Autism Spectrum Disorders, ADHD",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 16th 2021",dateEndSecondStepPublish:"May 14th 2021",dateEndThirdStepPublish:"July 13th 2021",dateEndFourthStepPublish:"October 1st 2021",dateEndFifthStepPublish:"November 30th 2021",remainingDaysToSecondStep:"22 days",secondStepPassed:!1,currentStepOfPublishingProcess:2,editedByType:null,kuFlag:!1,biosketch:"Dr. Sandro Misciagna received his degree in medicine at the Catholic University in Rome. As a clinician, he has worked in different neurological departments in Italian hospitals, Alzheimer’s clinics, neuropsychiatric clinics, and neurological rehabilitative departments as the Neurological Department and Stroke Unit of Belcolle Hospital in Viterbo, Italy.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"103586",title:null,name:"Sandro",middleName:null,surname:"Misciagna",slug:"sandro-misciagna",fullName:"Sandro Misciagna",profilePictureURL:"https://mts.intechopen.com/storage/users/103586/images/system/103586.jpg",biography:"Dr. Sandro Misciagna was born in Italy in 1969. He received a degree in medicine in 1995 and another in neurology in 1999 from The Catholic University, Rome. From 1993 to 1995, he was involved in research of cerebellar functions. From 1994 to 2003, he attended the Neuropsychological department involved in research in cognitive and behavioural disorders. From 2001 to 2003, he taught neuropsychology, neurology, and cognitive rehabilitation. In 2003, he obtained a Ph.D. in Neuroscience with a thesis on the behavioural and cognitive profile of frontotemporal dementia. Dr. Misciagna has worked in various neurology departments, Alzheimer’s clinics, neuropsychiatric clinics, and neuro-rehabilitative departments. In November 2016, he began working as a neurologist at Belcolle Hospital, Viterbo, where he has run the epilepsy centre since February 2019.",institutionString:"Ospedale di Belcolle",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"4",totalChapterViews:"0",totalEditedBooks:"3",institution:{name:"Ospedale di Belcolle",institutionURL:null,country:{name:"Italy"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"21",title:"Psychology",slug:"psychology"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"280415",firstName:"Josip",lastName:"Knapic",middleName:null,title:"Mr.",imageUrl:"https://mts.intechopen.com/storage/users/280415/images/8050_n.jpg",email:"josip@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copy-editing and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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In the meantime, in the field of medical blood-flow measurement, various diagnostic methods and diagnostic indices were proposed and had been standardized. By re-focusing on the empirical data from these diagnoses, the relationship between biosignals that many medical doctors had accumulated was revealed. In this chapter, new applications of statistical diagnosis methods and imaging technology are proposed.
Medical Doppler ultrasound systems are commonly used for various diagnostic applications, including examination of cardiac and abdomen. Figure 1 is the example of diagnostic image of a left ventricle inflow. The upper part of Fig. 1 (B-mode image) shows the tomogram of echo, and the lower part of Fig. 1 (D-mode image) shows the spectrum Doppler image. The D-mode image shows the blood-flow velocity at the mitral valve on the B-mode image. In the D-mode image, the horizontal axis is time and the vertical axis is the blood-flow velocity which corresponds to Doppler-shift frequency. The waveform displayed in the lower part of D-mode image is an electrocardiogram (ECG). The amplitude of echo reflected from tissue constructs B-mode image, and the Doppler-shift signal from blood-flow constructs D-mode image.
Diagnostic image of Doppler ultrasound system.
Currently, time-sharing blood-flow measurement in Doppler ultrasound system appeared. But the time-sharing systems have many problems caused by acoustic velocity range limitation. To address these problems, the mathematical models based on system identification methods were proposed in this chapter. One of the system identification models has ECG as input and has short time Fourier transform (STFT) image parameters as outputs. Based on this model, a new gap-filling system introduced in Section 3 was developed. It can fill the 100 ms gap.
Doppler ultrasound diagnoses of the left ventricle inflow and outflow are very helpful. The diagnostic techniques using the peak velocity waveform (the envelope trace of the Doppler spectra) are the standards of blood-flow measurement. Synchronizing with the systolic phase and diastolic phase that ECG shows, the mimetic diagrams of the outflow from an aortic valve and the inflow from a mitral valve are shown in Fig. 2. The features of these waveforms are measured and evaluated, and they are used as the standards of cardiac disease diagnoses [2]. New diagnostic technology that applies causal relationship between biosignals (here, they are an ECG and a Doppler trace waveform) introduced in Section 4 was developed. Furthermore, many medical doctors make standards of causal relationship between these biosignals over the time of 30 years or more. They are suitable to be applied to the statistical models.
Heartbeat indices and their measurement guidance.
In order to express the causal relationship between biosignals, the mathematical model that consists of an input, an output, and a black-box is suitable. The black-box model is shown in Fig. 3. Since the input
Black-box model and linear dynamic system.
ECG, the Doppler waveform and the spectrum Doppler image were used for the causal relationship analyses. In Section 3, the mathematical model based on ECG and Doppler imaging parameters was used. In Section 4, the mathematical model based on ECG and Doppler trace waveform was used. Figure 4 shows the expression of ARX model frequently used in these investigations.
Numerical formula of ARX model.
When ECG and the Doppler waveform are applied to ARX model, the system is expressed as the model of Fig. 5. Two coefficient sequences
ARX model based on ECG and the Doppler waveform.
The physical meaning of ARX model can be explained with IIR type digital filter. Figure 6 is a digital filter with two inputs:
By using system identification, the causal relationship between
Thus the response of black-box model can be presumed by system identification using statistical data. Noise rejection technology of the Doppler waveform and gap-filling technology of Doppler image based on system identification are introduced. Moreover, possibilities such as technology that complements the lack part of ECG and automatic diagnostic technology (computer aided diagnosis [CAD]) using statistical data will be introduced to another opportunity.
ARX model denoted by digital filter expression.
Doppler velocity range limitation caused by transmit pulse repetition frequency (PRF) is a serious matter with blood-flow measurement. There exists a trade-off between depth range and velocity range.
In order to display the B-mode image and the D-mode image simultaneously in real time, a time-sharing scanning is needed. Normally, PRF of approximately 4 kHz is employed, taking the propagation time of acoustic wave and the attenuation in the living body into account. Use of higher PRF has many advantages. For example, the scanning line density is increased, and as a result B-mode images with higher azimuth resolution can be obtained. In addition, the velocity range of D-mode is expanded. On the other hand, the higher PRF reduces the imaging depth range. Therefore, information concerning deeper regions cannot be obtained. So PRF control is complicated, especially with time-sharing of B-mode scanning and D-mode scanning.
In current Doppler ultrasound systems, it is difficult to optimize both the D-mode image quality and the B-mode image quality simultaneously. A new Doppler gap-filling algorithm based on ARX model was developed, which had ECG as an external input, for detecting the high-speed blood-flow in heart, carotid arteries, etc., and for generating high-quality D-mode images. The conventional gap-filling algorithm of D-mode image suffers from various problems such as presence of noise or artifacts and poor reproducibility in the rapid velocity change.
Examples of the interleave scanning and the segment scanning.
Examples of time-sharing transmission/reception of the interleave scanning and the segment scanning are shown in Fig. 7. The B-mode images (100 beams, 6 kHz PRF, approximately 7 cm depth) with a frame rate of 30 Hz are obtained in both Fig. 7(b) and (c). However, the velocity ranges obtained simultaneously in D-mode differ. The velocity range of D-mode is set to 6 kHz in the interleave scanning shown in Fig. 7(b). But the velocity range of D-mode is set to 12 kHz in the segment scanning shown in Fig. 7(c). Since both B-mode and D-mode become discontinuous in the case of segment scanning, the gap-filling algorithm is needed in D-mode signal processing and interpolation processing is needed in D-mode image processing. Moreover, when the gap-filling algorithm is applied, both quality of the image and the audio are degraded. On the other hand, since the PRFs can be set to B-mode and D-mode independently, the Doppler velocity range can be expanded.
Simultaneous real-time display of B-mode and D-mode by the segment scanning has been used for many years. The gap-filling algorithm fills in the gaps of IQ signal (shown in Fig. 8). The gap is filled with the predicted waveform that is generated based on an autoregressive (AR) model. Recently, many improved gap-filling algorithms have been reported [3]. For example, in one method, the gaps are filled in from both time directions; in another method, narrow-band noise is used as a source of signal; and in another method, an autoregressive moving average (ARMA) model is used.
Figure.8 shows a Doppler ultrasound system with the conventional gap-filling algorithm. The received beam is generated in digital beam former (DBF). The output of DBF is sent to the envelope detection processing in which echo intensity is detected. Then the echo intensity signal is sent to the B-mode image processing, and then displayed as the B-mode image. In the spectrum Doppler processing section, the Doppler-shift signal is detected by quadrature detection. Since the detected signal contains low-velocity and high-power components called clutter from tissues (vessel walls etc.), the wall filter rejects the clutters except for blood-flow components. The gap-filling algorithm interpolates the gaps of the D-mode image and the Doppler audio caused by the segment scanning.
Doppler ultrasound system with conventional gap-filling algorithm.
Figure 9 shows the details of gap-filling algorithm. It shows system identification and linear prediction based on AR model. Figure 10 shows the timing chart of its signal-processing shown in Fig. 9. To estimate the output for Gap(B), band limiting is applied to a white noise source. Prediction is performed in both forward and reverse directions, and blending is performed with
System identification and prediction based on AR model.
Timing chart of gap-filling algorithm.
There are two problems specific to Doppler ultrasound systems employing the conventional gap-filling algorithm. The first problem is that artifacts are newly generated in the predicted output although the low-frequency components already have been removed in the wall filter. For these artifacts, not only D-mode image quality but also audio quality is degraded. The second problem is that discontinuity of D-mode images becomes greater when there are sudden changes in the spectrum (rapid changes in blood-flow velocity). In many cases, the predicted D-mode image has horizontal lines and spikelike noises that are observed near the gaps. Figure 11 shows the D-mode images of a portal vein with moderate changes in velocity. Figure 11(a) is the D-mode image obtained by interleave scanning and Fig. 11(b) is the D-mode image obtained by segment scanning with the conventional gap-filling algorithm. Fig. 11(a) is smooth and free of artifacts, while periodic spikelike noise and low-frequency components are observed in Fig. 11(b).
Artifacts in D-mode image caused by the conventional gap-filling algorithm.
The conventional gap-filling algorithm based on AR model (or ARMA model) uses the noise and the predicted output itself as feedback inputs. So it tends to generate the waveforms consisting of multiple changeless frequency components. When noise is
To improve image quality of B-mode, a longer gap than D-mode is required. But D-mode image quality is markedly degraded when the gap of B-mode becomes long. It is also important to track blood-flow changes due to pulsation for D-mode image quality. But the gap-filling algorithm based on AR model is insufficient. Diagnostic performance will be substantially improved if long gaps are not filled with changeless spectra but filled with changeful spectra.
A new algorithm that can reduce spectrum artifacts and stabilize rapid changes in velocity in order to overcome problems shown in Section 3.3 was developed. This algorithm uses not IQ signals but Doppler spectrum parameters as input, and is based on ARX model [5]. The outline of a new D-mode image processing is shown in Fig. 12. After quadrature detection IQ signals are generated. IQ signals are processed by the wall filter and STFT sequentially, and D-mode image is generated. The waveforms with 600 ms time lack (left time-domain IQ signals) show the output of the wall filter, which removes low-frequency clutter. The output of STFT shows the momentary spectra (right frequency-domain periodgram). STFT conducts frequency analysis and carries out the time shift image of spectra [6, 7].
New D-mode image processing.
A new gap-filling algorithm in Fig. 12, which is based on ARX model, is shown in Figs. 13 and 14. Figure 13 is a block diagram of system identification for the new gap-filling algorithm. Figure 14 shows ECG and the D-mode image of left ventricular inflow. The spectrum shown in the lower side of Fig. 14 shows mean velocity (
System identification of a new algorithm.
Single-peak spectrum parameter model.
The formulas of spectrum parameters are shown in equations (5)-(7). For example, the power spectrum is expressed as
The system that has
New gap-filling algorithm based on ARX model.
The predicted output of ARX model
The first term and third terms of equation (8) are similar as equation (3). However, the second term of equation (8) means a time-variant system and can generate changeful spectra.
Simulations were applied to confirm ARX model processing and its performance. The data used for simulation was left ventricular inflow, which exhibits rapid changes in velocity. The gap of segment scan was set to 100 ms, which is one order of magnitude larger than the conventional system. The simulation result is shown in Fig. 16. Time 0 to 1 s is a continuous D-mode image (without segment scanning), and time 1 to 2 s is a discontinuous D-mode image (segment scanning). Domains indicated by (a) are actual spectra, and domains indicated by (b) in the figure are spectra estimated by ARX model. Condition of the simulation is as follows: ARX prediction order is 9 to 12, the segment gap is 100 ms, and the blending time is 16.7 ms.
Simulation of predicted spectra based on ARX model.
This result shows that spikelike noises and low-frequency artifacts are reduced compared with the conventional algorithm. Moreover, it is possible to obtain a changeful D-mode image under conditions of rapid changes in velocity and larger segment gaps.
The blood-flow diagnoses by Doppler ultrasound system have become popular recently. Peak velocity of blood-flow (STFT envelope waveform) is traced automatically in this system. But valve signals are mixed with the blood-flow signals in the heart. So automatic blood-flow measurements are not correctly recorded. To solve this problem, the mathematical model that has ECG as an input and has Doppler waveform as an output was applied. Using system identification method, a new valve-rejection algorithm was developed [8, 9].
Figure 17 is a Doppler ultrasound diagnostic image for left ventricular outflow. Doppler ultrasound system traces
In Fig. 17, ECG (green line) is displayed with
Example of left ventricular outflow.
Figure 18 shows a Doppler ultrasound system and automatic blood-flow measurement system. DBF generates echo beams from transmitted and received ultrasound signals.
Automatic blood-flow measurement system.
Many clinical data sets (
System identification model of Doppler waveform.
Example of data sets for system identification.
Figure 21 shows the valve-rejection algorithm using the coefficient sequences of Fig. 19 obtained by system identification.
Figure 22 shows the waveforms of Fig. 21.
Ideal
Signal-processing waveforms of system prediction in
Parametric models such as ARMAX were used as mathematical models [10]. A parametric model is shown in formula (10).
Here,
Many clinical data sets of left ventricular outflow using a Doppler ultrasound system were acquired. Because outflow varies with individual differences, combined data from numerous volunteers was used for evaluation. Combined data has different waveforms, heartbeat cycles, and blood-flow sensitivities. Figure 23 shows combined
Several mathematical models were applied and evaluated. Orders of several models such as ARX model, ARMAX model, output error model (OE model), etc., were optimized, respectively. Next, the model fitnesses were evaluated by root-mean-square (RMS) errors. OE model was chosen for the valve-rejection algorithm because of the smallest RMS error.
Combined data sets for system identification.
Volunteers’ blood-flow data.
Finally, OE model was chosen as the optimal one for left ventricular outflow. In order to verify its performance of valve-rejection algorithm, the additional data other than volunteer A, B and C was needed. A different volunteer\'s data (data D) is shown in Fig. 25. Simulation results of the valve-rejection algorithm are shown in Fig. 25(b) using data D. During the first, second, third, and fifth heartbeat cycles,
Verification of valve rejection algorithm.
Based on the mathematical model that combined an ECG and biosignals (ultrasound Doppler image parameters, etc.), the system identification method to heart\'s blood flows was applied. With combination of the image parameter and the ECG, the effectiveness of a new gap-filling algorithm was confirmed. Moreover, with combination of the Doppler blood-flow waveform and the ECG, noises in heart\'s blood-flow measurement, such as valve regurgitation, were removed, and reliable automatic measurement of left ventricle outflow was realized. System identification using such a statistical method will be an important component for automatic measurement and diagnosis.
Coronavirus disease (COVID-19) is an infectious respiratory tract infection with common symptoms including high fever, dry cough, and fatigue; it is caused by a newly discovered coronavirus (SARS-CoV-2). The novel coronavirus was first detected in Wuhan, China’s Hubei province in December 2019 and spread rapidly in China, and then, worldwide. The World Health Organization (WHO) declared the COVID-19 epidemic a pandemic on March 11, 2020 [1]. A total of 70,829,855 confirmed cases of COVID-19 and 1,605,091 deaths had been reported worldwide as of 10:25 AM, December 14, 2020, with cases continuing to increase [2].
\nThe COVID-19 epidemic is unprecedented in modern times and has become a major public health problem, not only for China but worldwide [3, 4, 5, 6]. The increasing number of cases posed a major challenge to hospitals treating individuals with COVID-19 symptoms and has resulted in a serious shortage of medical supplies and health personnel, especially in intensive care units [7, 8, 9]. During the COVID-19 pandemic, healthcare workers were infected and forced to fight against a deadly virus while lacking personal protective equipment [9, 10, 11]. The International Council of Nurses (ICN) reported that approximately 10% of worldwide cases are healthcare workers and that more than 20,000 healthcare workers were infected. It was reported that the epidemic cost the lives of at least 1,500 nurses and many other healthcare workers [12].
\nThis fatal situation has caused all healthcare professionals, especially nurses who work directly with sick or quarantined individuals, to face serious physical and psychological problems. Working with protective equipment that restricts breathing and movement makes it difficult to meet basic physiological needs such as eating, drinking, going to the toilet, and sleeping [13, 14, 15]. In addition, conditions such as limited hospital resources, long working hours, physical fatigue, infection risk, lack of protective equipment, disruption of sleep patterns, loneliness, and being separated from their families cause nurses’ mental health also to be at risk [4, 8, 16]. All these stressors cause noticeable psychological changes for nurses working closely with patients [6, 8, 15]. It has been reported that nurses experience major mental problems such as primary and secondary traumatic stress, job burnout, compassion fatigue, and moral injury during this process [4, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26].
\nAlthough it is difficult to provide both safe physical environmental conditions and mental security, having a safe work environment is the right of every health worker. Nevertheless, although physical security measures are prioritized for nurses serving in difficult conditions, mental security measures are either insufficient or ignored completely, despite nurses’ mental health being very important for controlling an epidemic [15, 16, 27]. For this reason, conducting appropriate prevention activities and planning prevention strategies for future pandemic situations is important to support nurses psychologically and to protect their mental health. This study discusses the mental problems of nurses caring for COVID-19 patients and psychological empowerment studies for nurses; it will make an important contribution to the health security of nurses.
\nThis chapter deals with the mental problems faced by nurses during the COVID-19 pandemic at an international level. Academic literature and other public databases for the year 2020, when COVID-19 cases started to appear and studies on the subject were carried out all over the world, were examined. Articles published in electronic databases including CINAHL, Cochrane Library, PubMed, Web of Science, Science Direct and Google Scholar, Scopus and related internet websites (WHO, ICN and APA) were used. Firstly, a comprehensive search of peer-reviewed journals were completed based on a wide range of key terms including “COVID-19”, “health security”, “mental health”, “mental problems”, “nurse”, “pandemic”, “psychological empowerment”, “psychological resilience”, “primary traumatic stress”, “secondary traumatic stress”, “burnout”, “compassion fatigue”, and “moral injury”.
\nThe literature search was carried out between November–December 2020 and 140 academic studies were reached as a result of the searching. The data were obtained from 53 international papers and 3 different websites (WHO, ICN and APA) that address the mental problems of COVID-19 nurses and contain prevention and strengthening studies on this issue that threatens international health safety were included in the study. In line with the results obtained from the studies, the mental problems experienced by COVID-19 nurses were grouped under five headings, and some suggestions about protect and strengthen mental health at international level were presented.
\nNurses are anonymous heroes, playing critical roles in disease prevention and diagnosis, and providing primary health care services including prevention, treatment, and rehabilitation [28]. They have been and continue to be at the forefront of combating infectious diseases such as COVID-19, leading the way in developing best practices in disease management and clinical security [13, 29, 30]. However, despite this obvious situation, for centuries nurses have found themselves trying to explain the importance of their profession, the reason for its existence, and its indispensability.
\nThe World Health Assembly has announced the year 2020 as the “International Year of Nurses and Midwives” [31]. Because of the COVID-19 pandemic, the nursing profession is on the world agenda, just as it suits the name of the year, and nurses have started to show that they are “A Pioneering Voice in World Health” [32]. This year, which created a global awareness for nurses, once again emphasized the importance of necessary health security measures in harmony with the changing roles of nurses.
\nThe high prevalence, highly contagious nature, and associated morbidity and mortality rates of COVID-19 in the general population of many countries create an unprecedented demand for health and social care services worldwide [13, 14]. This demand has transformed the role of nurses beyond patient care, which is regarded as a security boat that integrates different professions and communities to reduce the risk of the COVID-19 pandemic and ensure effective communication [13]. The addition of new ways of nursing, which is already demanding in terms of attention and care, has made working in the COVID-19 environment extremely stressful. Nurses try to adapt to new protocols to the “new normal”, beyond just experiencing an increase in the intensity of their work in this process. Concomitantly, due to the increasing number of patients, the need for more nurses in clinics, emergency rooms and intensive care units where care is provided for COVID-19, and the interruption of work due to the infection of health personnel in this process, has constituted an extra workload for all healthcare professionals [14].
\nNurses who work at maximum capacity also experience various problems such as deciding which critically ill patients may be allocated to the intensive care unit and which patients can be provided with a respiratory device; they accompany the end-of-life journey of both the patient and the family in the face of deterioration faster than they are accustomed to [33, 34, 35]. At the same time, because of isolation precautions and rules, patient relatives are not able to be with the patients, which results in nurses’ providing the necessary support and establishing remote communication between the patient and relatives, giving nurses additional responsibilities [36]. Protective measures such as masks, visors, and social distancing applied in this process make interaction difficult and patients, and nurses suffer from communication problems such as not being able to see each other’s faces or hear what they are saying [5, 14].
\nIn addition, factors such as limited resources of hospitals, lack of protective equipment, longer shifts, increased workload, new tasks and procedures, exposure to COVID-19 and risk of transmitting the infection, inadequate access to COVID-19 testing if symptoms develop, uncertainty as to whether their organization will support their needs if infection develops, support for additional needs (such as food, accommodation, transportation) as working hours increase, obligation to work in new units (such as those who are not intensive care nurses to serve in the intensive care unit), dilemmas with teammates, prioritizing care for specific patients, watching patients die alone, different pathologies seen in addition to COVID-19, neglect of personal and family needs, social distancing from loved ones, inadequate communication, and exposure to insufficient information make nurses’ compliance even more difficult [8, 11, 37]. Nurses experience many complications at the same time in this process, such as inadequacy, uncertainty, fear, and change, and not only need physical but also mental support.
\nWhen determining innovative ways to provide an adequate workforce during the pandemic period, it is important that everything applied is safe for staff and patients [30]. The WHO called on governments and healthcare leaders to address persistent threats to the health and security of healthcare workers and patients in the COVID-19 pandemic and emphasized that no country, hospital, or clinic can keep their patients safe unless they first keep healthcare workers safe [38]. In this regard, the importance of mental security as well as physical security has been emphasized. The psychological effects of the infection itself should not be neglected for healthcare professionals.
\nWhile the COVID-19 crisis continues, situations such as the dismissal of nurses in some areas, reducing workforce and granting leaves, calling back retired nurses for help due to the growing demand for nursing services to combat the COVID-19 outbreak, or suspension of leave has made health care even more difficult [13, 35, 39]. Most of the nurses were not allowed to go home due to lack of staff: to meet their staffing needs many organizations have asked healthcare professionals treating COVID-19 patients to continue working until they show symptoms of the disease [13].
\nAlthough these different regulations made by governments are important for the protection of groups and society at risk of COVID-19 infection, it supports the stigmatization and exclusion of nurses [24, 40, 41]. Being able to report difficulties without worrying about being stigmatized or blamed is very important for both nurses and others to dare seek help [5, 14, 42]. Nurses’ mental problems should be detected early, and their access to mental health services should be provided for the security of the entire society, not just nurses or healthcare professionals [35].
\nNurses are not only exposed to physical risks, but have also faced concerns over the impact of COVID-19 on their own lives and families, as well as long working hours and work environment security [13, 19]. The susceptibility to psychiatric disorders has increased, especially in nurses who directly care for infectious patients in critical and intensive care units [7, 30]. Studies conducted in centers and units providing COVID-19 care in different parts of the world have reported that the mental health of nurses has been significantly affected and that nurses experience psychological problems [6, 15, 43, 44, 45].
\nIt has been determined that the most common psychological effects in nurses were fear, despair, anxiety, depression, and post-traumatic stress symptoms [19, 20, 21, 37, 46, 47]. Worldwide studies on mental problems that occur as a cause or consequence of these psychiatric disorders showed that nurses are facing primary and secondary traumatic stress, job burnout, compassion fatigue and moral injury [4, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]. To better understand the mental problems seen in nurses in the pandemic it is necessary to define these concepts and carry out studies within the scope of combating these problems.
\nPrimary (direct) traumatic stress, stress that is directly perceived by the individual, is a threat to health security, along with time constraints, patient expectations, lack of social support, and inadequate coping [25]. Among the factors that directly lead to stress for nurses in the COVID-19 pandemic are staff shortages, lack of personal protective equipment, being in an unfamiliar environment or care system, and concerns about lack of organizational support. In addition, the psychological conflict between health care workers’ responsibility to care for patients and their behavior to protect themselves from a potentially deadly virus can also lead to stress [14].
\nNurses who are at bedside 24 hours a day, seven days a week, have the highest occupational stress compared to other groups [14]. Studies on COVID-19 show that work-related stress is especially prominent in nurses [4, 20, 21, 24]. Work-related stress in nurses leads to decreased physical function, emotional exhaustion, desensitization, decreased personal success, low job satisfaction, and personnel transfer [25]. Although nurses seem to function in this process, they also experience accompanying physical and psychological symptoms due to background long-term stress exposure.
\nSecondary (indirect) traumatic stress, defined as the stress of helping people who are in pain or who were traumatized and recovered, develops without direct sensory traces because of long-term exposure of the helping individual to the traumatic event and the continuous repetition of an event with unpleasant details [25, 48]. The more traumatic the event and the greater the contact with the patient, the greater the risk of secondary traumatic stress formation [24]. It emerges due to risk factors such as the unpredictability and increased infection rate during the COVID-19 emergency, repeated exposure to trauma, and witnessing patients suffering. In addition, a more intense empathic approach to patients that causes greater vulnerability of healthcare workers also leads to secondary traumatic stress [10, 24, 40].
\nSecondary traumatic stress, which is considered an occupational hazard, is very common in nurses, especially those working in emergency, oncology, psychiatry, and pediatrics departments [25]. Healthcare workers who directly encountered COVID-19 patients intensive care units and in critical centers reserved for COVID-19, experienced higher secondary traumatic stress than others. [17, 18, 25]. Secondary trauma has been studied more than primary trauma. Its prevalence brings with it other serious problems such as anorexia, insomnia, fatigue, anger, apathy, unwillingness, hopelessness and depression.
\nBurnout is a psychological syndrome characterized by emotional exhaustion associated with prolonged exposure to occupational stress (depletion of emotional resources), desensitization (developing cynical attitudes about patients), and decreased professional success (a sense of negative self-evaluation) [18, 49, 50]. The deadly and uncontrollable nature of COVID-19 with currently no known effective cure and the relatively high infection and mortality rate among healthcare workers trigger feelings of anxiety and stress. Problems such as social stigma, lack of personal protective equipment, and heavy workload pave the way for burnout in healthcare workers [49].
\nRecent studies report that nurses caring for COVID-19 patients experience more burnout than others [18, 19, 23, 25]. Burnout can have serious consequences for patients, healthcare professionals, and institutions. This not only results in poor physical and mental health consequences, lack of motivation, absenteeism, and low morale, but also in deterioration of the quality of care provided by the staff affected, decrease in patients’ satisfaction levels, an increase in health-related infections, and high mortality among patients [18, 49].
\nCompassion fatigue is seen as contextually interchangeable with secondary traumatic stress; it is generally known as a combination of secondary traumatic stress and burnout symptoms [8]. Compassion fatigue is a job-related stress response that is considered a “maintenance cost” in healthcare workers. It is closely related to professional satisfaction, personnel transfer rate, and nursing quality [7, 25]. During pandemics such as COVID-19, intensive care nurses witness patient suffering and death more frequently than before, and in addition are responsible for decisions regarding allocation and use of resources, which is why they carry a high risk of compassion fatigue [7].
\nStudies report that among all healthcare professionals, nurses who provide uninterrupted care to patients and who show an approach with empathy are at risk for compassion fatigue and that their health status, job performance, and professional satisfaction levels are affected [8, 25]. It was seen that nurses, who have been in contact and interacting with COVID-19 patients for a long time, also experience compassion fatigue [19, 23, 25]. Nurses experiencing compassion fatigue may use harmful coping methods such as absenteeism, leaving work, despondency, social isolation, alcohol-substance use, and overeating [7].
\nMoral injury is a concept used to describe psychological distress caused by acts that violate a person’s ethical or moral rules or acts that lack said rules [8, 51]. The pandemic is a difficult time during which healthcare professionals experience dilemmas in the triage of COVID-19 patients, for instance where they must decide which of two patients will get the emergency room’s only remaining ventilator. As a result of this decision the nurse may experience feelings such as guilt, shame, or remorse, which will negatively affect all aspects of life. Although the health worker tells himself/herself that he/she is following the protocol and doing his/her best, he/she will think that he/she has violated moral values [14, 52].
\nAll healthcare workers and all frontline workers such as emergency first responders are subject to moral injury during this time [51, 52]. However, the measurement tools and studies to diagnose the painful and powerful internal struggles experienced by healthcare workers during the COVID-19 pandemic and the resulting moral injury are insufficient [52, 53], although some scales have been developed to describe this process [22, 26]. Moral injury negatively affects ability to function and performance; it can also lead to depression and post-traumatic stress disorder [5, 52]. In addition, nurses are prone to quit their jobs if they feel that they are not sufficiently supported by organizations and the government [14].
\nThe topic of focusing on the mental health of health professionals has been brought to the agenda during the COVID-19 pandemic [54]. It is necessary to have spiritual endurance to overcome this unprecedented situation: nurses have the need be supported by the employer, the team, and professional and community resources. During this time, the applause given to healthcare professionals every day of the week across Europe has been a morale boost for healthcare professionals. However, this is not a satisfactory solution. In addition to that, healthcare professionals need to feel that their needs are met and that they are safe in all environments [14]. In this regard, the development of psychological resilience of nurses through both individual, social, and organizational studies comes to the fore.
\nResilience (psychological resilience) is defined as the process and result of successfully adapting to difficult life experiences through mental, emotional, and behavioral flexibility and adaptation to internal and external demands. The ways individuals see and relate to the world in the face of problems, the availability and quality of social resources, and certain coping strategies contribute to adaptation [55]. Psychological resilience, which increases the ability to cope with and resist difficulties, ensures that healthcare workers are less affected by the consequences of the stress they face and are more successful in crisis management, and it helps them recover more easily after the pandemic [56]. At the same time, resilience plays a role as a protective factor so that mental problems might not develop in all individuals exposed to high adverse effects or crisis situations [37, 57].
\nWhen all these factors are considered, it is seen that high levels of psychological resilience are important for healthcare workers to effectively combat COVID-19 infection and maintain mental health. To increase nurses’ psychological resilience, needs should be determined early, initiatives should be made to reduce or eliminate factors that have negative effects on mental health, and approaches to increase mental health protective factors should be determined [15, 25, 48, 56, 58]. Taking effective psychological support measures, and removing and balancing the fear, anxiety or sadness caused by the epidemic will help healthcare professionals to feel psychologically safe. This may also improve crisis resistance, adaptation, and prevent mental disorders [43].
\nStudies have shown that nurses battling against the COVID-19 epidemic need mental support [4, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]. It is necessary to determine and implement appropriate and effective strategies for the development of psychological resilience and mental health protection of nurses. For future epidemics like COVID-19, protective and supportive measures to protect health professionals’ mental health should be taken in addition to measures to protect their physical health. Organizational, managerial, physiological, social, and psychological protective measures are needed in this regard.
\nOrganizational support, or the degree to which an organization provides resources, empowerment, encouragement, and communication for an individual to perform their functions effectively, is a vital factor that also contributes to organizational success. There is a positive relationship between higher organizational support levels, patient satisfaction in nurses, and positive outcomes [59]. Resilience intervention implemented in response to the COVID-19 epidemic, focuses on self-care, self-efficacy, and social relationships, as well as providing quick access to mental health consultation and support when needed [60]. Nine evidence-based organizational strategies are recommended to encourage participation of health system leaders and managers and to reduce burnout: acknowledging and evaluating the problem; harnessing the power of effective leadership; developing, and implementing targeted interventions; improving the community at work; using rewards and incentives wisely; aligning values and strengthening culture; promoting flexibility and work-life integration; providing resources to promote endurance and self-care; and facilitating and financing organizational science [61].
\nProviding managerial support in this period is of critical importance. Strategies must be developed so that health system leaders and managers may stay well in these turbulent and sad times, and so that organizations become able to lead the repair and revitalization of a post-COVID-19 world [61]. Nurse managers play a vital role in providing evidence-based measures, supportive organizational policies, and a safe and secure work environment to support the mental, psychological, and emotional health of nurses, thereby relieving their fears or anxieties [4, 59]. Managers should also assess the mental state of nurses and identify high risk individuals, provide psychological support and counseling, and collaborate with expert teams to provide professional psychological services when needed [43]. In this regard, managers need to adopt the issue of mental security of nurses with a holistic approach that recognizes the broader impact of the emotional distress caused by COVID-19. This will lead nurses to feel safe psychologically and will encourage them to communicate security concerns and problem-solving strategies to managers [11].
\nTo eliminate psychological stress responses to the COVID-19 pandemic and to create personal resilience, it is important to implement physical measures. The working environment and daily life should be optimized to support proper nutrition, rest, sleep, and security requirements [5, 14, 60]. Also, hospitals should be careful about physical security issues in addition to meeting basic physiological needs, such as busy working times and provide protective equipment against infections [8, 61]. Ensuring physical security will help prevent symptoms such as fear, anxiety, fatigue, and exhaustion, and thereby protect mental health.
\nThere is good evidence that social support has a stronger effect than material support and that it is often protective for mental health [48, 51]. Social and peer support has been identified as an important protective factor against trauma effect and general mental well-being [8]. Start and end of shifts create natural opportunities for interactions to develop friendship and teamwork. Social support should be developed within the team, and friend relationships of potential shift colleagues should be strengthened for them to monitor each other’s well-being [5, 14]. Social support has a positive effect on nurses’ professional satisfaction, commitment to work, health, and well-being. Sufficient social support is needed for healthcare professionals for them to effectively manage stressful events, including emergencies, disasters, and infectious disease outbreaks [59]. The social isolation measures taken to minimize the transmission in the COVID-19 pandemic forces nurses to stay away from family, social circles, and team colleagues, which makes it difficult for nurses to reach the adequate support system that is very important.
\nWith a few exceptions, hospitals all around the world are generally not designed or adaptable to provide continued emotional support to its staff. Despite that, there are many services that a healthcare worker can use when he or she feels in distress. However, these systems are rarely used [54]. In many countries, consultancy teams that include psychiatrists have been established to reduce the effects of COVID-19, and healthcare professionals have been provided with counseling and psychotherapy services; various mental support programs have also been developed to address the mental health problems among health professionals [33, 50].
\nWuhan University RenMin Hospital and the Mental Health Center in Wuhan formed psychological intervention teams that included four groups of healthcare personnel. The first, the psychosocial response team (consisting of hospital directors and press officers), is the team that coordinates the work and promotional tasks of the management team. The second, the psychological intervention technical support team (consisting of senior psychological intervention professionals), is responsible for formulating psychological intervention materials and guidelines and providing technical guidance and supervision. The third, psychological intervention medical team, consisting mostly of psychiatrists, participate in clinical psychological intervention for healthcare professionals and patients. The fourth group, the psychological helpline team (consisting of volunteers trained in psychological assistance to cope with the COVID-19 pandemic), provides telephone guidance for dealing with mental health problems [16].
\nPsychiatrists have published various guidelines to prevent the development of mental problems worldwide and promote social and peer support, psychological support and resilience programs have been developed, and online and telephone mental support lines have been established [5, 14, 33, 39, 54]. In addition, consultation liaison psychiatric support was emphasized concerning the necessity for awareness studies, nutritional and exercise supplement, communication skills, stress management and relaxation skills, psychoeducational interventions, small group therapies, cognitive restructuring, yoga, music and art therapy, grief counseling, pharmacological treatment, and suicide protocols for severe cases. [41, 42].
\nAs a result, the COVID-19 pandemic has shown us that there are numerous resilience initiatives in various forms, both those specific to COVID-19 and those more general. Digital interventions common in recent years are increasingly used to improve healthcare and outcomes. Within the scope of COVID-19 measures, it has been discovered that it is not always possible to work elbow to elbow, and the best applications can be carried out without contact are possible through online environments. It is very important to develop studies in this regard, considering their positive effect on nurses.
\nIn Table 1, the causes and results of the mental problems experienced by nurses in the COVID-19 pandemic are stated, and attempts to protect and strengthen the mental health of nurses are summarized.
\nMental problems | \nCauses | \nEffects | \nInterventions | \n
---|---|---|---|
Primary traumatic stress | \n\n
| \n\n
| \n\n
| \n
Secondary traumatic stress | \n\n
| \n\n
| \n|
Job burnout | \n\n
| \n\n
| \n|
Compassion fatigue | \n\n
| \n\n
| \n|
Moral injury | \n\n
| \n\n
| \n
Mental problems of COVID-19 nurses and prevention strategies.
Combating epidemics is an important responsibility that both affects all layers of society deeply and increases the physical and psychological burden of healthcare workers. Nurses caring for COVID-19 patients experience serious mental problems because they must help individuals in pain, stay with them, provide help for relatives, and perhaps witness a patient’s death. Although physical security measures such as maintaining adequate protective equipment are prioritized in this process, it is observed that mental security measures are mostly ignored.
\nEnsuring and maintaining nurses’ security is an important indicator of the effective management of the pandemic process. For this reason, it is necessary to determine the factors that may cause mental problems in nurses, diagnose these problems, provide appropriate physical and working conditions, and maintain psychosocial support. For this to happen, it is necessary to provide both emergency psychological first aid and long-term psychological assistance services and carry out follow-up studies. It is suggested that institutions and leaders follow policies on professional mental health support, initiate appropriate studies for services to be provided in the context of future crises, and create an action plan.
\nFocusing on supporting nurses during and after the pandemic is of great importance for the future of nursing and the security of society. It is also expected that this support for the welfare of nurses will continue when the health care system returns to pre-pandemic condition. To protect and maintain the well-being of nurses will enable them to assume their caring roles and responsibilities wherever they are located practice more effectively and competently. In this regard, that the role of nurses in the universal healthcare system involves a very important key role in meeting the care needs of the society and ensuring security, should not be forgotten.
\nThere is no conflict of interest.
"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges".
\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.
",metaTitle:"About Open Access",metaDescription:"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges.\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.",metaKeywords:null,canonicalURL:"about-open-access",contentRaw:'[{"type":"htmlEditorComponent","content":"The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\\n\\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\\n\\nAt IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
\\n\\n“By “open access” to [peer-reviewed research literature], we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited” (reference: http://www.budapestopenaccessinitiative.org)
\\n\\nOAI-PMH
\\n\\nAs a firm believer in the wider dissemination of knowledge, IntechOpen supports the Open Access Initiative Protocol for Metadata Harvesting (OAI-PMH Version 2.0). Read more
\\n\\nLicense
\\n\\nBook chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
\\n\\nPeer Review Policies
\\n\\nAll scientific works are Peer Reviewed prior to publishing. Read more
\\n\\nOA Publishing Fees
\\n\\nThe Open Access publishing model employed by IntechOpen eliminates subscription charges and pay-per-view fees, enabling readers to access research at no cost. In order to sustain operations and keep our publications freely accessible we levy an Open Access Publishing Fee for manuscripts, which helps us cover the costs of editorial work and the production of books. Read more
\\n\\nDigital Archiving Policy
\\n\\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
\\n"}]'},components:[{type:"htmlEditorComponent",content:'The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\n\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\n\nAt IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
\n\n“By “open access” to [peer-reviewed research literature], we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited” (reference: http://www.budapestopenaccessinitiative.org)
\n\nOAI-PMH
\n\nAs a firm believer in the wider dissemination of knowledge, IntechOpen supports the Open Access Initiative Protocol for Metadata Harvesting (OAI-PMH Version 2.0). Read more
\n\nLicense
\n\nBook chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
\n\nPeer Review Policies
\n\nAll scientific works are Peer Reviewed prior to publishing. Read more
\n\nOA Publishing Fees
\n\nThe Open Access publishing model employed by IntechOpen eliminates subscription charges and pay-per-view fees, enabling readers to access research at no cost. In order to sustain operations and keep our publications freely accessible we levy an Open Access Publishing Fee for manuscripts, which helps us cover the costs of editorial work and the production of books. Read more
\n\nDigital Archiving Policy
\n\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
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I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. I have served as the editor for many books, been a member of the editorial board in science journals, have published many papers and hold many patents.",institutionString:null,institution:{name:"Sheffield Hallam University",country:{name:"United Kingdom"}}},{id:"54525",title:"Prof.",name:"Abdul Latif",middleName:null,surname:"Ahmad",slug:"abdul-latif-ahmad",fullName:"Abdul Latif Ahmad",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"20567",title:"Prof.",name:"Ado",middleName:null,surname:"Jorio",slug:"ado-jorio",fullName:"Ado Jorio",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universidade Federal de Minas Gerais",country:{name:"Brazil"}}},{id:"47940",title:"Dr.",name:"Alberto",middleName:null,surname:"Mantovani",slug:"alberto-mantovani",fullName:"Alberto Mantovani",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"12392",title:"Mr.",name:"Alex",middleName:null,surname:"Lazinica",slug:"alex-lazinica",fullName:"Alex Lazinica",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/12392/images/7282_n.png",biography:"Alex Lazinica is the founder and CEO of IntechOpen. 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