Search results for: nursing interventions classification

Authors: Mais Nijim, Rama Devi Chennuboyina, Waseem Al Aqqad

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Advances in spatial and spectral resolution of satellite images have led to tremendous growth in large image databases. The data we acquire through satellites, radars and sensors consists of important geographical information that can be used for remote sensing applications such as region planning, disaster management. Spatial data classification and object recognition are important tasks for many applications. However, classifying objects and identifying them manually from images is a difficult task. Object recognition is often considered as a classification problem, this task can be performed using machine-learning techniques. Despite of many machine-learning algorithms, the classification is done using supervised classifiers such as Support Vector Machines (SVM) as the area of interest is known. We proposed a classification method, which considers neighboring pixels in a region for feature extraction and it evaluates classifications precisely according to neighboring classes for semantic interpretation of region of interest (ROI). A dataset has been created for training and testing purpose; we generated the attributes by considering pixel intensity values and mean values of reflectance. We demonstrated the benefits of using knowledge discovery and data-mining techniques, which can be on image data for accurate information extraction and classification from high spatial resolution remote sensing imagery.

Keywords: remote sensing, object recognition, classification, data mining, waterbody identification, feature extraction

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Authors: Faisal Muhibuddin, Ani Dijah Rahajoe

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This in-depth exploration, through a systematic literature review, scrutinizes the nuanced role of data mining in the classification of criminal activities. The research focuses on investigating various methodological aspects and recent developments in leveraging data mining techniques to enhance the effectiveness and precision of crime categorization. Commencing with an exposition of the foundational concepts of crime classification and its evolutionary dynamics, this study details the paradigm shift from conventional methods towards approaches supported by data mining, addressing the challenges and complexities inherent in the modern crime landscape. Specifically, the research delves into various data mining techniques, including K-means clustering, Naïve Bayes, K-nearest neighbour, and clustering methods. A comprehensive review of the strengths and limitations of each technique provides insights into their respective contributions to improving crime classification models. The integration of diverse data sources takes centre stage in this research. A detailed analysis explores how the amalgamation of structured data (such as criminal records) and unstructured data (such as social media) can offer a holistic understanding of crime, enriching classification models with more profound insights. Furthermore, the study explores the temporal implications in crime classification, emphasizing the significance of considering temporal factors to comprehend long-term trends and seasonality. The availability of real-time data is also elucidated as a crucial element in enhancing responsiveness and accuracy in crime classification.

Keywords: data mining, classification algorithm, naïve bayes, k-means clustering, k-nearest neigbhor, crime, data analysis, sistematic literature review

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Authors: Kemal Polat

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In this paper, three feature weighting methods have been used to improve the classification performance of diabetic retinopathy (DR). To classify the diabetic retinopathy, features extracted from the output of several retinal image processing algorithms, such as image-level, lesion-specific and anatomical components, have been used and fed them into the classifier algorithms. The dataset used in this study has been taken from University of California, Irvine (UCI) machine learning repository. Feature weighting methods including the fuzzy c-means clustering based feature weighting, subtractive clustering based feature weighting, and Gaussian mixture clustering based feature weighting, have been used and compered with each other in the classification of DR. After feature weighting, five different classifier algorithms comprising multi-layer perceptron (MLP), k- nearest neighbor (k-NN), decision tree, support vector machine (SVM), and Naïve Bayes have been used. The hybrid method based on combination of subtractive clustering based feature weighting and decision tree classifier has been obtained the classification accuracy of 100% in the screening of DR. These results have demonstrated that the proposed hybrid scheme is very promising in the medical data set classification.

Keywords: machine learning, data weighting, classification, data mining

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Authors: Awole Seid, Yosef Zenebe, Hadgu Gerensea, Kebede Haile Misgina

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Background: Clinical competence is one of the ultimate goals of nursing education. Clinical skills are more than successfully performing tasks; it incorporates client assessment, identification of deficits and the ability to critically think to provide solutions. Assessment of clinical competence, particularly identifying gaps that need improvement and determining the educational needs of nursing students have great importance in nursing education. Thus this study aims determining clinical self-efficacy of final year school of nursing students in three universities of Tigray Region. Methods: A cross-sectional study was conducted on 224 final year school of nursing students from department of nursing, psychiatric nursing, and midwifery on three universities of Tigray region. Anonymous self-administered questionnaire was administered to generate data collected on June, 2017. The data were analyzed using SPSS version 20. The result is described using tables and charts as required. Logistic regression was employed to test associations. Result: The mean age of students was 22.94 + 1.44. Generally, 21% of students have been graduated in the department in which they are not interested. The study demonstrated 28.6% had poor and 71.4% had good perceived clinical self-efficacy. Beside this, 43.8% of psychiatric nursing and 32.6% of comprehensive nursing students have poor clinical self-efficacy. Among the four domains, 39.3% and 37.9% have poor clinical self- efficacy with regard to ‘Professional development’ and ‘Management of care’. Place of the institution [AOR=3.480 (1.333 - 9.088), p=0.011], interest during department selection [AOR=2.202 (1.045 - 4.642), p=.038], and theory-practice gap [AOR=0.224 (0.110 - 0.457), p=0.000] were significantly associated with perceived clinical self-efficacy. Conclusion: The magnitude of students with poor clinically self efficacy was high. Place of institution, theory-practice gap, students interest to the discipline were the significant predictors of clinical self-efficacy. Students from youngest universities have good clinical self-efficacy. During department selection, student’s interest should be respected. The universities and other stakeholders should improve the capacity of surrounding affiliate teaching hospitals to set and improve care standards in order to narrow the theory-practice gap. School faculties should provide trainings to hospital staffs and monitor standards of clinical procedures.

Keywords: clinical self-efficacy, nursing students, Tigray, northern Ethiopia

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Authors: Nasar Aldian Ambark Shashoa

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Classification with a dimension reduction based on Bayesian approach is proposed in this paper . The first step is to generate a sample (parameter) of fault-free mode class and faulty mode class. The second, in order to obtain good classification performance, a selection of important features is done with the discrete karhunen-loeve expansion. Next, the Bayes test for minimum error is used to classify the classes. Finally, the results for simulated data demonstrate the capabilities of the proposed procedure.

Keywords: analytical redundancy, fault detection, feature extraction, Bayesian approach

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Authors: Yaser Miaji, Mohammed Aloryani

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The rise of recent applications in everyday implementation like videoconferencing, online recreation and voice speech communication leads to pressing the need for novel mechanism and policy to serve this steep improvement within the application itself and users‟ wants. This diversity in web traffics needs some classification and prioritization of the traffics since some traffics merit abundant attention with less delay and loss, than others. This research is intended to reinforce the mechanism by analysing the performance in application according to the proposed mechanism implemented. The mechanism used is quite direct and analytical. The mechanism is implemented by modifying the queue limit in the algorithm.

Keywords: traffic classification, IPv6, internet, application categorization

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Authors: Vesile Unver, Tulay Basak, Hatice Ayhan, Ilknur Cinar, Emine Iyigun, Nuran Tosun

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The aim of this study is to analyze the effects of the hybrid simulation. In this simulation, types standardized patients and task trainers are employed simultaneously. For instance, in order to teach the IV activities standardized patients and IV arm models are used. The study was designed as a quasi-experimental research. Before the implementation an ethical permission was taken from the local ethical commission and administrative permission was granted from the nursing school. The universe of the study included second-grade nursing students (n=77). The participants were selected through simple random sample technique and total of 39 nursing students were included. The views of the participants were collected through a feedback form with 12 items. The form was developed by the authors and “Patient intervention self-confidence/competence scale”. Participants reported advantages of the hybrid simulation practice. Such advantages include the following: developing connections between the simulated scenario and real life situations in clinical conditions; recognition of the need for learning more about clinical practice. They all stated that the implementation was very useful for them. They also added three major gains; improvement of critical thinking skills (94.7%) and the skill of making decisions (97.3%); and feeling as if a nurse (92.1%). In regard to the mean scores of the participants in the patient intervention self-confidence/competence scale, it was found that the total mean score for the scale was 75.23±7.76. The findings obtained in the study suggest that the hybrid simulation has positive effects on the integration of theoretical and practical activities before clinical activities for the nursing students.

Keywords: hybrid simulation, clinical practice, nursing education, nursing students

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Authors: Mohammad Khazaei, Reza Rostami, Hassan Gharayagh Zandi, Ruhollah Basatnia, Mahboubeh Ghayour Najafabadi

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Introduction: This study delves into the influence of diverse interventions on brain functional connectivity among young male basketball players. Given the significance of understanding how interventions affect cognitive functions in athletes, particularly in the context of basketball, this research contributes to the growing body of knowledge in sports neuroscience. Methods: Three distinct groups were selected for comprehensive investigation: the Motivational Interview Group, Placebo Consumption Group, and Ritalin Consumption Group. The study involved assessing brain functional connectivity using various frequency bands (Delta, Theta, Alpha, Beta1, Beta2, Gamma, and Total Band) before and after the interventions. The participants were subjected to specific interventions corresponding to their assigned groups. Results: The findings revealed substantial differences in brain functional connectivity across the studied groups. The Motivational Interview Group exhibited optimal outcomes in PLI (Total Band) connectivity. The Placebo Consumption Group demonstrated a marked impact on PLV (Alpha) connectivity, and the Ritalin Consumption Group experienced a considerable enhancement in imCoh (Total Band) connectivity. Discussion: The observed variations in brain functional connectivity underscore the nuanced effects of different interventions on young male basketball players. The enhanced connectivity in specific frequency bands suggests potential cognitive and performance improvements. Notably, the Motivational Interview and Placebo Consumption groups displayed unique patterns, emphasizing the multifaceted nature of interventions. These findings contribute to the understanding of tailored interventions for optimizing cognitive functions in young male basketball players. Conclusion: This study provides valuable insights into the intricate relationship between interventions and brain functional connectivity in young male basketball players. Further research with expanded sample sizes and more sophisticated statistical analyses is recommended to corroborate and expand upon these initial findings. The implications of this study extend to the broader field of sports neuroscience, aiding in the development of targeted interventions for athletes in various disciplines.

Keywords: electroencephalography, Ritalin, Placebo effect, motivational interview

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Authors: Abdul-Monim Batiha

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Aim: The aim of this study were to correlate Jordanian cancer patients’ quality of life and fatigue with selected variables (age, sex, religion, marital status, level of education, type of cancer, number of people living in the same household, type of radiotherapy, dose of radiotherapy, and hemoglobin level). Background: Radiotherapy and chemotherapy remain devastating agents that altered patients’ normal lives. Methods: A correlational design was used in this study to 80 cancer patients and required radiotherapy treatment using a convenience sampling procedure. Results: No significant differences were found in the relationship between quality of life scores and selected variables. A significant negative relationship was found between quality of life scores and the side effects of radiotherapy treatment. Significant positive relationships were found between fatigue scores measured by Piper Fatigue Scale and cancer complications, and radiotherapy side effects. Conclusion: Cancer patients’ quality of life and fatigue are affected by radiotherapy’s side effects and cancer complications. Implications for Nursing: Nurses should try to prevent and manage the negative side effects of radiotherapy and complications of cancer. Such an initiative would serve to design specific nursing interventions that have the potential to help patients enjoy their lives and perform their activities.

Keywords: cancer patients, piper fatigue scale, fatigue, quality of life, radiotherapy

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Authors: Szu-Yi Chang, Jiin-Ru Rong

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This paper discussed a case involving a young female patient with schizophrenia. The patient's condition was deteriorating, and she was becoming increasingly reliant on her family to take care of her, and as her father did not understand the illness well and was afraid that others will learn about the presence of a mentally ill individual in their family, he and the patient's mother were thus unable to cope with the patient's deteriorating condition, which in turn caused her to suffer from a lack of self-confidence and low self-esteem. The patient received nursing care from July 26th to October 25th, 2017, during which counseling, family visits, and phone interviews were carried out, and her condition was monitored. By referring to the practical ability indicators for community psychiatric mental health nursing that were developed by the psychiatric mental health nurses' association of the Republic of China, defining categories such as 'self-construction,' 'self-management,' 'disease management,' and 'family nursing,' and incorporating indicators for empowerment and various skills into the steps and strategies used for nursing care, we will able to help the patient to construct her own identity, raise her self-esteem, improve her ability to independently perform activities of daily living, strengthen her disease management ability, and gradually build up her life management skills. The patient's family was also encouraged to communicate more among themselves, so as to align them with the nursing care objectives of improving the patient's ability to adapt to community life and her disease. The results indicated that the patient was able to maintain her mental stability within her community. By implementing effective self-management and maintaining a routine life, the patient was able to continue her active participation in community work and rehabilitation activities. Improvements were also achieved with respect to family role issues by establishing mutual understanding among the patient's family members and gaining their support. It is recommended that mental health nurses can leverage their community mental health nursing skills and the related strategies to promote adaptation to community life among mental life patients.

Keywords: community psychiatric mental health nursing, family nursing, schizophrenia, self-management

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Authors: Sohni Siddiqui, Anja Schultze-Krumbholz

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Our lives now revolve more around online-related tasks, as the internet has become a necessity. One of the disturbance concerns with high internet usage is the multiplication of cyber-associated risky behaviors such as cyber aggression and/or cyberbullying. Cyber Bullying is an emerging issue that needs immediate attention from many stakeholders such as parents, doctors, school administrators, policymakers, researchers, and others, especially in the COVID-19 pandemic when online learning has been adopted as an instructional strategy, and there is a continuous rise in cyberbullying cases. The aim of the article is to review existing successful and emerging interventions designed to control bullying and cyberbullying by engaging individuals through teachers’ professional development and adopting a whole-school approach. The study identified the strengths and limitations of the programs and suggested improvements to existing interventions. Preparing interventions with a strong theoretical framework, integrating applications of emerging theories in interventions, promoting proactive and reactive strategies in combination, beginning with the baseline needs assessment surveys, reducing digital time and digital divide among parents and children, promoting the concept of lead trainer, peer trainer, and hot spots, focusing on physical activities, use of landmarks are some of the recommendations proposed by authors. In addition to face-to-face intervention, the researchers recommend updating and improving previous intervention programs with games and apps. Especially in the time of pandemic crises, when face-to-face interactions are limited and cyberbullying is triggered, the use of apps, web-based interventions, and games can be an effective way to control electronic perpetration and victimization.

Keywords: anti bullying programs, cyber bullying, individualized trainings, teachers’ professional development, whole school interventions

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Authors: Sarah-Anne Schumann, Chintan Shah, Sandeep Ponniah, Syeachia Dennis

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Effective interventions to prevent childhood obesity include limiting sugar-sweetened beverage intake (SOR: B, longitudinal study), school and home based strategies to reduce total screen time and increase physical activity, behavioral and dietary counseling, and support for parents and families (SOR: A, meta-analysis of randomized and non-randomized controlled trials). Risk factors for childhood obesity include maternal pre-pregnancy weight, high infant birth weight, early infant rapid weight gain and maternal smoking during pregnancy which may provide opportunities to intervene and prevent childhood obesity (SOR: B, meta-analysis of observational studies).

Keywords: childhood, obesity, prevent obesity, interventions to prevent obesity

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Authors: Kaisar Akhir, Azhar Slamet

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This study aims to provide recommendations for addressing marine plastic litter at the ocean tourism destinations in Indonesia sustainably through technological interventions in the framework of the National Action Plan for Marine Litter Management 2018-2025. In Indonesia, marine tourism is a rapidly growing economic sector. However, marine tourism destinations are facing a global challenge called marine plastic litter. Marine plastic litter is a threat to those destinations since it has potential impacts on the reduction of marine environmental sustainability, the health of tourists and local communities as well as tourism business income. Since 2018, the Indonesian government has passed and promulgated the National Plan of Action on Marine Litter Management 2018-2025. This national action plan consists of three important key aspects of interventions (i.e., societal effort, technological application, and institutional coordination) and five strategies for addressing marine litter in Indonesia, in particular, to address 70% of marine plastic litter by 2025. The strategies include 1) National movement for raising awareness of stakeholders, 2) Land-based litter management, 3) Litter management at the sea and coasts, 4) Funding mechanism, institutional strengthening, monitoring, and law enforcement, and 5) Research and development. In this study, technological interventions around the world and in Indonesia are reviewed and analyzed on their relevance to the national action plan based on five criteria. As a result, there are twelve kinds of technological interventions recommended to be implemented for addressing marine plastic litter in the marine tourism destinations in Indonesia.

Keywords: marine litter management, marine plastic litter, national action plan, ocean sustainability, ocean tourism destination, technological interventions

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Authors: Dechen Tshering Vogel, Johannes T. Heverhagen, Bernard Kiss, Spyridon Arampatzis

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In addition to computed tomography (CT), contrast enhanced ultrasound (CEUS), and magnetic resonance imaging (MRI) are being increasingly used for imaging of renal lesions. The aim of this prospective study was to compare the classification of complex cystic renal lesions using the Bosniak classification with CEUS and MRI to CT. Forty-eight patients with 65 cystic renal lesions were included in this study. All participants signed written informed consent. The agreement between the Bosniak classifications of complex renal lesions ( ≥ BII-F) on CEUS and MRI were compared to that of CT and were tested using Cohen’s Kappa. Sensitivity, specificity, positive and negative predictive values (PPV/NPV) and the accuracy of CEUS and MRI compared to CT in the detection of complex renal lesions were calculated. Twenty-nine (45%) out of 65 cystic renal lesions were classified as complex using CT. The agreement between CEUS and CT in the classification of complex cysts was fair (agreement 50.8%, Kappa 0.31), and was excellent between MRI and CT (agreement 93.9%, Kappa 0.88). Compared to CT, MRI had a sensitivity of 96.6%, specificity of 91.7%, a PPV of 54.7%, and an NPV of 54.7% with an accuracy of 63.1%. The corresponding values for CEUS were sensitivity 100.0%, specificity 33.3%, PPV 90.3%, and NPV 97.1% with an accuracy 93.8%. The classification of complex renal cysts based on MRI and CT scans correlated well, and MRI can be used instead of CT for this purpose. CEUS can exclude complex lesions, but due to higher sensitivity, cystic lesions tend to be upgraded. However, it is useful for initial imaging, for follow up of lesions and in those patients with contraindications to CT and MRI.

Keywords: Bosniak classification, computed tomography, contrast enhanced ultrasound, cystic renal lesions, magnetic resonance imaging

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Authors: Zhang Shuqi, Liu Dan

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For the problems in intelligent network anomaly traffic detection models, such as low detection accuracy caused by the lack of training samples, poor effect with small sample attack detection, a classification model enhancement method, F-ACGAN(Flow Auxiliary Classifier Generative Adversarial Network) which introduces generative adversarial network and adversarial training, is proposed to solve these problems. Generating adversarial data with category labels could enhance the training effect and improve classification accuracy and model robustness. FACGAN consists of three steps: feature preprocess, which includes data type conversion, dimensionality reduction and normalization, etc.; A generative adversarial network model with feature learning ability is designed, and the sample generation effect of the model is improved through adversarial iterations between generator and discriminator. The adversarial disturbance factor of the gradient direction of the classification model is added to improve the diversity and antagonism of generated data and to promote the model to learn from adversarial classification features. The experiment of constructing a classification model with the UNSW-NB15 dataset shows that with the enhancement of FACGAN on the basic model, the classification accuracy has improved by 8.09%, and the score of F1 has improved by 6.94%.

Keywords: data imbalance, GAN, ACGAN, anomaly detection, adversarial training, data augmentation

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Authors: Mary Holstein

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Objective: The aim of this study was to evaluate nurse leader confidence in emergency management and disaster preparedness in the state of Texas. My project was a replication study of a survey conducted in 2022 by Reedy et al, for members of the Northwest Organization for Nurse Leaders (NONL). Background: In 2022, the American Association of Colleges of Nursing (AACN) approved new essentials for academic nursing education programs to demonstrate competencies in disaster management, yet no integration of such information into nursing curriculum had been reported in the literature. Research replicated by members of the Texas Organization for Nursing Leadership suggested significant gaps in nurse leader confidence across roles and in structured education that prepares nurse leaders across the spectrum of experience to lead in a crisis. Methods: An exploratory, cross-sectional survey used a sample of 86 RNs who were members of TONL. Results: Results replicated comparable results with significant variance in nurse leader confidence across roles, experience, and previous disaster-related education. Positive associations regarding nurse leaders' confidence in managing disasters were obvious with more advanced positions, further education, and mandatory training. Conclusions: Nursing leaders in Texas lack mandatory and structured education to prepare for emergency and disaster management. The call for mandatory emergency management training and disaster preparedness for nurse leaders remains unmet.

Keywords: confidence, disaster, education, emergency

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Authors: F. Moghtaderi, M. Burke, J. Troelsen

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There has been a significant decline in active travel as well as the massive increase use of car-dependent travel mode in many countries during past two decades. Evidential risks for people’s physical and mental health problems are followed by this increased use of motorized travel mode. These problems range from overweight and obesity to increasing air pollution. In response to these rising concerns, local councils and other interested organizations around the world have introduced a variety of initiatives regarding reduce the dominance of cars for the daily journeys. However, the nature of these kinds of interventions, which related to the human behavior, make lots of complexities. People’s travel behavior and changing this behavior, has two different aspects. People’s attitudes and perceptions toward the sustainable and healthy modes of travel, and motorized travel modes (especially private car use) is one these two aspects. The other one related to people’s behavior change processes. There are no comprehensive model in order to guide policy interventions to increase the level of succeed of such interventions. A comprehensive theoretical framework is required in accordance to facilitate and guide the processes of data collection and analysis to achieve the best possible guidelines for policy makers. Regarding this gaps in the travel behavior change research, this paper attempted to identify and suggest a multidimensional framework in order to facilitate planning interventions. A structured mixed-method is suggested regarding the expand the scope and improve the analytic power of the result according to the complexity of human behavior. In order to recognize people’s attitudes, a theory with the focus on people’s attitudes towards a particular travel behavior was needed. The literature around the theory of planned behavior (TPB) was the most useful, and had been proven to be a good predictor of behavior change. Another aspect of the research, related to the people’s decision-making process regarding explore guidelines for the further interventions. Therefore, a theory was needed to facilitate and direct the interventions’ design. The concept of the transtheoretical model of behavior change (TTM) was used regarding reach a set of useful guidelines for the further interventions with the aim to increase active travel and sustainable modes of travel. Consequently, a combination of these two theories (TTM and TPB) had presented as an appropriate concept to identify and design implemented travel behavior change interventions.

Keywords: behavior change theories, theoretical framework, travel behavior change interventions, urban research

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Authors: Souhir Chelly, Chahida Harizi, Aicha Hechaichi, Sihem Aissaoui, Leila Ben Ayed, Maha Bergaoui, Mohamed Kouni Chahed

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Introduction: The International Classification of Primary Care (ICPC) is part of the morbidity classification system. It had 17 chapters, and each is coded by an alphanumeric code: the letter corresponds to the chapter, the number to a paragraph in the chapter. The objective of this study is to show the utility of this classification in the coding of the reasons for demand for care in Primary health care (PHC), its advantages and limits. Methods: This is a cross-sectional descriptive study conducted in 4 PHC in Ariana district. Data on the demand for care during 2 days in the same week were collected. The coding of the information was done according to the CISP. The data was entered and analyzed by the EPI Info 7 software. Results: A total of 523 demands for care were investigated. The patients who came for the consultation are predominantly female (62.72%). Most of the consultants are young with an average age of 35 ± 26 years. In the ICPC, there are 7 rubrics: 'infections' is the most common reason with 49.9%, 'other diagnoses' with 40.2%, 'symptoms and complaints' with 5.5%, 'trauma' with 2.1%, 'procedures' with 2.1% and 'neoplasm' with 0.3%. The main advantage of the ICPC is the fact of being a standardized tool. It is very suitable for classification of the reasons for demand for care in PHC according to their specificity, capacity to be used in a computerized medical file of the PHC. Its current limitations are related to the difficulty of classification of some reasons for demand for care. Conclusion: The ICPC has been developed to provide healthcare with a coding reference that takes into account their specificity. The CIM is in its 10th revision; it would gain from revision to revision to be more efficient to be generalized and used by the teams of PHC.

Keywords: international classification of primary care, medical file, primary health care, Tunisia

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Authors: B. S. Harish, M. B. Revanasiddappa

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Due to rapid growth of text documents in digital form, automated text classification has become an important research in the last two decades. The major challenge of text document representations are high dimension, sparsity, volume and semantics. Since the terms are only features that can be found in documents, selection of good terms (features) plays an very important role. In text classification, feature selection is a strategy that can be used to improve classification effectiveness, computational efficiency and accuracy. In this paper, we present a quantitative analysis of most widely used feature selection (FS) methods, viz. Term Frequency-Inverse Document Frequency (tf
idf ), Mutual Information (MI), Information Gain (IG), CHISquare (x2), Term Frequency-Relevance Frequency (tf
rf ), Term Strength (TS), Ambiguity Measure (AM) and Symbolic Feature Selection (SFS) to classify text documents. We evaluated all the feature selection methods on standard datasets like 20 Newsgroups, 4 University dataset and Reuters-21578.

Keywords: classifiers, feature selection, text classification

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Authors: Lily Parm, Irma Nool, Liina Männiksaar, Mare Tupits, Ivi Prits, Merilin Kuhi, Valentina Raudsepp

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Background: The results of previous clinical satisfaction surveys show that nursing students swhounderw entinternships in the pediatricwardhadthelowestsatisfactioncomparedtootherwards, but the quality of students' practicaltrainingexperienceisanimportant determinant in nursing education. The aim of theresearchwastodescribenursingstudents` assessment to the clinical learning environment and supervision in pediatric wards Method: Theresearchisquantitative. All studentswhohadpracticaltraining in the pediatric ward participated in the study (N = 39). FordatacollectionClinicalLearningEnvironment, Supervision, and NurseTeacher (CLES + T) evaluationscalewasused, wherethescalewasanswered on a 5-point Likert scale. In addition, 10 backgroundvariableswereused in the questionnaire. IBM SPSS Statistics 28.0 wasusedfordataanalysis. Descriptive statistics and Spearmanncorrelationanalysiswasusedtofindcorrelatinsbetweenbackgroundvariables and satisfaction with supervision.Permissiontoconductthestudy (No 695) hasbeenobtainedbytheEthicsCommittee of theInstituteforHealthDevelopment. Results: Of therespondents, 28 (71.8%) werefirst-year, 9 (23.1%) second-year and 2 (5.1%) fourth-yearstudents. Thelargestshare of the last practicaltrainigwas in nursing, with 27 (69.2%) respondents. Mainlythementorswerenursesfor 32 (82,1%) of students.Satisfactionwiththementoring (4.4 ± 0.83) and wardnursemanager`sleaderhiostyle (4.4 ± 0.7), ratedthehighest and therole of thenurseteacherwasratedthelowest (3,7 ± 0.83.In Spearmann'scorrelationanalysis, therewas a statisticallystrongcorrelationbetween a positiveattitudetowardsthesupervisor'ssupervision and receivingfeedbackfromthesupervisor (r =0.755; p <0.001), studentsatisfactionwithsupervision (r = 0.742; p <0.001), supervisionbased on cooperation (r = 0.77) and instructionbased on theprinciple of equalitythatpromotedlearning (r = 0.755; p <0.001). Conclusions: Theresults of theresearchshowedhighsatisfactionwiththesupervisionand therole of wardmanager. Stillbettercooperationisneededbetweenpracticalplacement and nursingschooltoenhancethestudents`satisfactionwithsupervision.

Keywords: CLES+T, clinical environment, nurse teacher, statisfaction, pediatric ward, mentorship

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Authors: Tianyi Wang, Hieyong Jeong, An Guo, Yuko Ohno

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Healthcare robot for assisting sit-to-stand (STS) performance had aroused numerous research interests. To author’s best knowledge, knowledge about how evaluating healthcare robot is still unknown. Robot should be labeled as fault if users feel demanding during STS when they are assisted by robot. In this research, we aim to propose a method to evaluate sit-to-stand assist robot through center of pressure (CoP), then classify different STS performance. Experiments were executed five times with ten healthy subjects under four conditions: two self-performed STSs with chair heights of 62 cm and 43 cm, and two robot-assisted STSs with chair heights of 43 cm and robot end-effect speed of 2 s and 5 s. CoP was measured using a Wii Balance Board (WBB). Bayesian classification was utilized to classify STS performance. The results showed that faults occurred when decreased the chair height and slowed robot assist speed. Proposed method for fault classification showed high probability of classifying fault classes form others. It was concluded that faults for STS assist robot could be detected by inspecting center of pressure and be classified through proposed classification algorithm.

Keywords: center of pressure, fault classification, healthcare robot, sit-to-stand movement

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Authors: Kristen Erekson, Sarah Spendlove Caswell

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Nurse educators have the challenge of training future nurses that will provide compassionate care to an increasingly diverse population of patients in a culturally sensitive way. One approach to this challenge is an immersive public and global health experience as part of the nursing program curriculum. Undergraduate nursing students at our institution are required to participate in a Public and Global Health course. They participate in a didactic preparatory course followed by a 3-to-4-week program in one of the following locations: The Czech Republic, Ecuador, Finland/Poland, Ghana, India, Spain, Taiwan, Tonga, an Honor Flight to Washington D.C. with Veterans, or in local (Utah) communities working with marginalized populations (including incarcerated individuals, refugees, etc.). The students are required to complete 84 clinical hours and 84 culture hours (which involve exposure to local history, art, architecture, customs, etc.). As Faculty, we feel strongly that these public and global health experiences help cultivate cultural awareness in our students and prepare nurses who are better prepared to serve a diverse population of patients throughout their careers. This presentation will highlight our experiences and provide ideas for other nurse educators who have an interest in developing similar programs in their schools but do not know where to start. Suggestions about how to start building relationships that can lead to these opportunities, along with logistics for continuing the programs, will be highlighted.

Keywords: global health nursing, nursing education, clinical education, public health nursing

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Authors: Vimarshi Sandamali Godigamuwa

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Background: Breast cancer is the leading cause of cancer mortality in women worldwide. Its incidence is increasing and young women affected more than ever. Nursing students are the future nurses who will have the opportunity to encourage and influence women to be aware of breast cancers. Objectives: To determine the level of knowledge, awareness and practices concerning of breast cancer among Sri Lankan student nurses. Methods: A descriptive cross sectional study was conducted on 150 nursing students who are in their 2nd and 3rd year studies by distributing a standard self-administered questionnaire. The completed questionnaire were retrieved, graded and scored. Results: Mean age of the respondents was 24.27; (SD=1.66) years and ranged from 20-30 years. Most of the students were female which was 85%. 32% of nursing students scored below 55% for the questionnaire and only 7.3% had good overall knowledge and awareness of breast cancer. Out of 128 female students 89.9% were answered that they know how to perform Breast Self Examination (BSE), out of which 37% of them performed BSE regularly. Only 33% were aware of recommended age for BSE and 10% were knew the recommended age for mammography. 9.3% were aware of frequency for Clinical Breast Examination on 20-39 years of age group. Of the female participants, 11.7% reported positive family history of breast cancer. Conclusion: Nursing students should explore to health educational programs on regular basis on breast cancer and its screening methods. Further studies are needed to identify reasons for not practicing BSE.

Keywords: breast cancer, student nurses, knowledge, awareness, practice, BSE

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Authors: Jameela Ali Alkrimi, Abdul Rahim Ahmad, Azizah Suliman, Loay E. George

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Red blood cells (RBCs) are among the most commonly and intensively studied type of blood cells in cell biology. The lack of RBCs is a condition characterized by lower than normal hemoglobin level; this condition is referred to as 'anemia'. In this study, a software was developed to isolate RBCs by using a machine learning approach to classify anemic RBCs in microscopic images. Several features of RBCs were extracted using image processing algorithms, including principal component analysis (PCA). With the proposed method, RBCs were isolated in 34 second from an image containing 18 to 27 cells. We also proposed that PCA could be performed to increase the speed and efficiency of classification. Our classifier algorithm yielded accuracy rates of 100%, 99.99%, and 96.50% for K-nearest neighbor (K-NN) algorithm, support vector machine (SVM), and neural network ANN, respectively. Classification was evaluated in highly sensitivity, specificity, and kappa statistical parameters. In conclusion, the classification results were obtained for a short time period with more efficient when PCA was used.

Keywords: red blood cells, pre-processing image algorithms, classification algorithms, principal component analysis PCA, confusion matrix, kappa statistical parameters, ROC

Procedia PDF Downloads 381

Authors: Hugo Reis, Isabel Rabiais

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The main purpose of the present study is to understand the nurse’s responsibility in preventing the septic shock in an oncological patient with febrile neutropenia submitted to chemotherapy. In order to do it, an integrative review of literature has been conducted. In the research done in many databases, it was concluded that only 7 out of 5202 articles compiled the entire inclusion standard present in the strict protocol of research, being this made up by all different methodologies. On the research done in the 7 articles it has resulted 8 text macro-units associated to different nursing interventions: ‘Health Education’; ‘Prophylactic Therapy Administration’; ‘Scales Utilization’; ‘Patient Evaluation’; ‘Environment Control’; ‘Performance of Diagnostic Auxiliary Exams’; ‘Protocol Enforcement/Procedure Guidelines’; ‘Antibiotic Therapy Administration’. Concerning the prevalence/result’s division there can be identified many conclusions: the macro-units ‘Patient Evaluation’, ‘Performance of Diagnostic Auxiliary Exams’, and ‘Antibiotic Therapy Administration’ present themselves to be the most prevalent in the research – 6 in 7 occurrences (approximately 85.7%). Next, the macro-unit ‘Protocol Enforcement/Procedure Guidelines’ presents itself as an important expression unit – being part of 5 out of the 7 analyzed studies (approximately 71.4%). The macro-unit ‘Health Education’, seems to be in the same way, an important expression unit – 4 out of the 7 (or approximately 57%). The macro-unit ‘Scales Utilization’, represents a minor part in the research done – it’s in only 2 out of the 7 cases (approximately 28.6%). On the other hand, the macro-units ‘Prophylactic Therapy Administration’ and ‘Environment Control’ are the two categories with fewer results in the research - 1 out of the 7 cases, the same as approximately 14.3% of the research results. Every research done to the macro-unit ‘Antibiotic Therapy Administration’ agreed to refer that the intervention should be strictly done, in a period of time less than one hour after diagnosing the fever, with the purpose of controlling the quick spread of infection – minimizing its seriousness. Identifying these interventions contributes, concluding that, to adopt strategies in order to prevent the phenomenon that represents a daily scenario responsible for the cost´s increase in health institutions, being at the same time responsible for the high morbidity rates and mortality increase associated with this specific group of patients.

Keywords: febrile neutropenia, oncology nursing, patient, septic shock

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Authors: Raeed Alanazi

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Introduction: Nursing students have a crucial role to play in the inhibition of infectious diseases and, therefore, must be trained in infection control and prevention modules prior to entering clinical settings. Simulations have been found to have a positive impact on infection control skills and the use of standard precautions. Aim: The purpose of this study was to use the four sources of self-efficacy in explaining the level of clinical self-efficacy and adherence to infection control practices in Saudi nursing students during simulation practice. Method: A cross-sectional design with convenience sampling was used. This study was conducted in all Saudi nursing schools, with a total number of 197 students participated in this study. Three scales were used simulation self- efficacy Scale (SSES), the four sources of self-efficacy scale (SSES), and Compliance with Standard Precautions Scale (CSPS). Multiple linear regression was used to test the use of the four sources of self-efficacy (SSES) in explaining level of clinical self-efficacy and adherence to infection control in nursing students. Results: The vicarious experience subscale (p =.044) was statistically significant. The regression model indicated that for every one unit increase in vicarious experience (observation and reflection in simulation), the participants’ adherence to infection control increased by .13 units (β =.22, t = 2.03, p =.044). In addition, the regression model indicated that for every one unit increase in education level, the participants’ adherence to infection control increased by 1.82 units (beta=.34= 3.64, p <.001). Also, the mastery experience subscale (p <.001) and vicarious experience subscale (p = .020) were shared significant associations with clinical self-efficacy. Conclusion: The findings of this research support the idea that simulation-based learning can be a valuable teaching-learning method to help nursing students develop clinical competence, which is essential in providing quality and safe nursing care.

Keywords: simulation-based learning, clinical self-efficacy, infection control, nursing students

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Authors: Jerzy Banski

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The basic aim of this study is to discuss and assess different classifications and research approaches to small towns that take their social and economic functions into account, as well as relations with surrounding areas. The subject literature typically includes three types of approaches to the classification of small towns: 1) the structural, 2) the location-related, and 3) the mixed. The structural approach allows for the grouping of towns from the point of view of the social, cultural and economic functions they discharge. The location-related approach draws on the idea of there being a continuum between the center and the periphery. A mixed classification making simultaneous use of the different approaches to research brings the most information to bear in regard to categories of the urban locality. Bearing in mind the approaches to classification, it is possible to propose a synthetic method for classifying small towns that takes account of economic structure, location and the relationship between the towns and their surroundings. In the case of economic structure, the small centers may be divided into two basic groups – those featuring a multi-branch structure and those that are specialized economically. A second element of the classification reflects the locations of urban centers. Two basic types can be identified – the small town within the range of impact of a large agglomeration, or else the town outside such areas, which is to say located peripherally. The third component of the classification arises out of small towns’ relations with their surroundings. In consequence, it is possible to indicate 8 types of small-town: from local centers enjoying good accessibility and a multi-branch economic structure to peripheral supra-local centers characterised by a specialized economic structure.

Keywords: small towns, classification, functional structure, localization

Procedia PDF Downloads 163

Authors: Diana Meeks

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The use of a virtual learning environment (VLE), via the Second Life Platform has been a positive experience to enhance critical thinking, for executive graduate nursing practicum students. Due to the interest of faculty and students, the opportunity to immerse students via a virtual learning environment to enhance critical thinking related to the nurse executive role was explored. The College of Nursing realized the potential to enhance critical thinking and incorporated the Second Life, virtual learning environment platform into their graduate nursing program within their executive practicum course. The results from students and faculty regarding this experience have been positive. Students state the VLE platform has enhanced their critical thinking and interaction with peers. To date, course refinement incorporating a Second Life, virtual learning environment for the nurse executive practicum students continues. As a result, a designated subject matter expert has been designated for this course. The development and incorporation of the VLE approach will be presented.

Keywords: nursing, virtual learning environment, critical thinking, VLE

Procedia PDF Downloads 446

Authors: Syed Basit Ali Shah Bukhari, Yan Qiang, Saad Abdul Rauf, Syed Saqlaina Bukhari

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Text Classification is the methodology to classify any given text into the respective category from a given set of categories. It is highly important and vital to use proper set of pre-processing , feature selection and classification techniques to achieve this purpose. In this paper we have used different ensemble techniques along with variance in feature selection parameters to see the change in overall accuracy of the result and also on some other individual class based features which include precision value of each individual category of the text. After subjecting our data through pre-processing and feature selection techniques , different individual classifiers were tested first and after that classifiers were combined to form ensembles to increase their accuracy. Later we also studied the impact of decreasing the classification categories on over all accuracy of data. Text classification is highly used in sentiment analysis on social media sites such as twitter for realizing people’s opinions about any cause or it is also used to analyze customer’s reviews about certain products or services. Opinion mining is a vital task in data mining and text categorization is a back-bone to opinion mining.

Keywords: Natural Language Processing, Ensemble Classifier, Bagging Classifier, AdaBoost

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Authors: Taner Ersoz, Filiz Ersoz, Seyma Ozbilge

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In this study, the clients who applied to a bank branch for loan were analyzed through data mining. The study was composed of the information such as amounts of loans received by personal and SME clients working with the bank branch, installment numbers, number of delays in loan installments, payments available in other banks and number of banks to which they are in debt between 2010 and 2013. The client risk profile was examined through Classification and Regression Tree (CART) analysis, one of the decision tree classification methods. At the end of the study, 5 different types of customers have been determined on the decision tree. The classification of these types of customers has been created with the rating of those posing a risk for the bank branch and the customers have been classified according to the risk ratings.

Keywords: client classification, loan suitability, risk rating, CART analysis

Procedia PDF Downloads 319