Search results for: nursing interventions classification

Authors: Ya-Yi Yen

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The operating room is a medical environment full of high-risk, high-complexity and high-cost. In addition to assuring patient safety, the operating room should effort on the efficient and safe medical quality for the surgical patients of high risk, elders, and children. If the nursing staffs of operation room carry on the pre-operative visiting prior to surgery, the patient's anxiety and complications are expected to be alleviated, and the hospitalization days may also be shortened. Purpose: Applying the information system to enhance pre-operative visiting, case tracking, and effectiveness recording Method: (I) Application the information system to screen cases by integrating the operation scheduling, and linking the severe surgery codes, for to shorten the time to track cases of operative visiting. Through the improvement, the time required decreased to 1.5 minutes per day from 20 minutes per day, and nursing staffs’ satisfaction with satisfaction for tracking and visiting procedure of case increased to 86% from 54%. (II)The electronic establishment of the operative visiting record enhanced the integrity of the operative visiting record. The integrity rate was rise to 92% from 66%, while nursing staffs’ satisfaction with the visiting record increased to 82% from 61.3%. Since information technology continues evolving, the application of information technology is helpful to the integration of nursing information, simplification of processes, and saving of man-hours. This article introduces the application of information systems to simplify the processes and improve the effectiveness of operation visiting and tracking, including the saving of time, improving the integrity rate of record, and improving the satisfaction of nursing staffs.

Keywords: effectiveness, information system, perioperative nursing, pre-operative visiting

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Authors: F. Jiménez, R. Jódar, M. Martín, G. Sánchez, G. Sciavicco

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Abstract—Attribute or feature selection is one of the basic strategies to improve the performances of data classification tasks, and, at the same time, to reduce the complexity of classifiers, and it is a particularly fundamental one when the number of attributes is relatively high. Its application to unsupervised classification is restricted to a limited number of experiments in the literature. Evolutionary computation has already proven itself to be a very effective choice to consistently reduce the number of attributes towards a better classification rate and a simpler semantic interpretation of the inferred classifiers. We present a feature selection wrapper model composed by a multi-objective evolutionary algorithm, the clustering method Expectation-Maximization (EM), and the classifier C4.5 for the unsupervised classification of data extracted from a psychological test named BASC-II (Behavior Assessment System for Children - II ed.) with two objectives: Maximizing the likelihood of the clustering model and maximizing the accuracy of the obtained classifier. We present a methodology to integrate feature selection for unsupervised classification, model evaluation, decision making (to choose the most satisfactory model according to a a posteriori process in a multi-objective context), and testing. We compare the performance of the classifier obtained by the multi-objective evolutionary algorithms ENORA and NSGA-II, and the best solution is then validated by the psychologists that collected the data.

Keywords: evolutionary computation, feature selection, classification, clustering

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Authors: Vishwa Joshi

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The study of mood recognition in the field of music has gained a lot of momentum in the recent years with machine learning and data mining techniques and many audio features contributing considerably to analyze and identify the relation of mood plus music. In this paper we consider the same idea forward and come up with making an effort to build a system for automatic recognition of mood underlying the audio song’s clips by mining their audio features and have evaluated several data classification algorithms in order to learn, train and test the model describing the moods of these audio songs and developed an open source framework. Before classification, Preprocessing and Feature Extraction phase is necessary for removing noise and gathering features respectively.

Keywords: music, mood, features, classification

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Authors: Jorge A. Ruiz-Vanoye, Ocotlán Díaz-Parra, Alejandro Fuentes-Penna, Daniel Vélez-Díaz, Edith Olaco García

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In this paper, we present the use of the discriminant analysis to select evolutionary algorithms that better solve instances of the vehicle routing problem with time windows. We use indicators as independent variables to obtain the classification criteria, and the best algorithm from the generic genetic algorithm (GA), random search (RS), steady-state genetic algorithm (SSGA), and sexual genetic algorithm (SXGA) as the dependent variable for the classification. The discriminant classification was trained with classic instances of the vehicle routing problem with time windows obtained from the Solomon benchmark. We obtained a classification of the discriminant analysis of 66.7%.

Keywords: Intelligent Transportation Systems, data-mining techniques, evolutionary algorithms, discriminant analysis, machine learning

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Authors: Ching-Yi Huang, Pi-Fen Cheng, Hui-Zhu Chen, Shi Ting Huang, Heng-Hua Wang

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This is a case of oral cancer after extensive resection and modified right lateral neck lymph node dissection followed by reconstruction with a skin flap. The nursing period lasted From September 25 to October 3, 2017, through observation, interview, physical assessment, and medical record review, the author identified the following nursing problems: acute pain, impaired oral mucous membrane, and body image change. During the nursing period, the author provided individual and overall nursing care and established mutual trust through the use of empathy. Author listened and eased the patient's physical indisposition, such as wound pain, we use medications and acupuncture massage to relieve pain. However, for oral mucosa change caused by surgery, provide continuous and complete oral care and oral exercise training to improve oral mucosal healing and restore swallowing function. In the body-image changes, guided him to express his feeling after the body-image change, and enhanced support and from the family, and encouraged him to attend head and neck cancer survivor alliance which allowed the patient to accept the altered body image and reaffirm self-worth. Hopefully, through sharing this nursing experience will help to the nursing care quality of nursing care for oral cancer patients after extensive resection and modified right lateral neck lymph node dissection followed by reconstruction with a skin flap.

Keywords: oral cancer, acute pain, impaired oral mucous membrane, body image change

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Authors: C. Lanzerstorfer

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The off-gas from the basic oxygen furnace (BOF), where pig iron is converted into steel, is treated in the primary ventilation system. This system is in full operation only during oxygen-blowing when the BOF converter vessel is in a vertical position. When pig iron and scrap are charged into the BOF and when slag or steel are tapped, the vessel is tilted. The generated emissions during charging and tapping cannot be captured by the primary off-gas system. To capture these emissions, a secondary ventilation system is usually installed. The emissions are captured by a canopy hood installed just above the converter mouth in tilted position. The aim of this study was to investigate the dependence of Zn and other components on the particle size of BOF secondary ventilation dust. Because of the high temperature of the BOF process it can be expected that Zn will be enriched in the fine dust fractions. If Zn is enriched in the fine fractions, classification could be applied to split the dust into two size fractions with a different content of Zn. For this air classification experiments with dust from the secondary ventilation system of a BOF were performed. The results show that Zn and Pb are highly enriched in the finest dust fraction. For Cd, Cu and Sb the enrichment is less. In contrast, the non-volatile metals Al, Fe, Mn and Ti were depleted in the fine fractions. Thus, air classification could be considered for the treatment of dust from secondary BOF off-gas cleaning.

Keywords: air classification, converter dust, recycling, zinc

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Authors: Jiahe Liu, Hongyang Yu, Feng Qian, Miao Luo

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SMPL-X was a powerful parametric human body model that included male, neutral, and female models, with significant gender differences between these three models. During the process of 3D human body reconstruction, the correct selection of standard templates was crucial for obtaining accurate results. To address this issue, we developed an efficient gender classification algorithm to automatically select the appropriate template for 3D human body reconstruction. The key to this gender classification algorithm was the precise analysis of human body features. By using the SMPL-X model, the algorithm could detect and identify gender features of the human body, thereby determining which standard template should be used. The accuracy of this algorithm made the 3D reconstruction process more accurate and reliable, as it could adjust model parameters based on individual gender differences. SMPL-X and the related gender classification algorithm have brought important advancements to the field of 3D human body reconstruction. By accurately selecting standard templates, they have improved the accuracy of reconstruction and have broad potential in various application fields. These technologies continue to drive the development of the 3D reconstruction field, providing us with more realistic and accurate human body models.

Keywords: gender classification, joint detection, SMPL-X, 3D reconstruction

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Authors: Claire Song

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Background: TikTok is an emerging social media platform creating an outlet for nurses to express and communicate their nursing experiences and stress related to nursing. Purpose: This study investigates the lived experiences of nursing burnout shared on TikTok. Method: The cross-sectional content analysis examines the video content, format, type, and quantitative indicators, including the number of likes and comments. Results: A total of 35 videos and 18616 comments were examined, published between November 2020 and May 2023. Combined, these 35 videos received 24859 comments and 1159669 of likes. Most of the videos included nurses, and 12 included nurses in professional attire. Three videos included interviewers in the video, but the rest of the videos were self-recorded. Four themes of nurses’ burnout experiences were identified: 1) high-intensity work environment, 2) negative internal perception, 3) culture of nursing work, and 4) poor teamwork experience. Conclusion: This study explored the description of nurses’ burnout experiences via a creative platform. Social media, such as TikTok, is a valuable outlet for healthcare providers to express and share their experiences. Future research might consider using the social media platform to explore coping strategies and resilience in nurses who experienced burnout.

Keywords: burnout, emotional wellbeing, nursing, social media

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Authors: Zhong Ma, Zhuping Wang, Congxin Liu, Xiangzeng Liu

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Satellite imagery classification is a challenging problem with many practical applications. In this paper, we designed a deep convolution neural network (DCNN) to classify the satellite imagery. The contributions of this paper are twofold — First, to cope with the large-scale variance in the satellite image, we introduced the inception module, which has multiple filters with different size at the same level, as the building block to build our DCNN model. Second, we proposed a genetic algorithm based method to efficiently search the best hyper-parameters of the DCNN in a large search space. The proposed method is evaluated on the benchmark database. The results of the proposed hyper-parameters search method show it will guide the search towards better regions of the parameter space. Based on the found hyper-parameters, we built our DCNN models, and evaluated its performance on satellite imagery classification, the results show the classification accuracy of proposed models outperform the state of the art method.

Keywords: satellite imagery classification, deep convolution network, genetic algorithm, hyper-parameter optimization

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Authors: Maria Janet, Anita David, P. Vijayasamundeeswarimaria

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Introduction: The main purpose of this study is to compare the effectiveness of web-based and blended learning on Paediatric Basic Life Support on competency among undergraduate nursing students in selected nursing colleges in Chennai. Materials and methods: A descriptive pre-test and post-test study design were used for this study. Samples of 100 Fourth year B.Sc., nursing students at Sri Ramachandra Faculty of Nursing SRIHER, Chennai, 100 Fourth year B.Sc., nursing students at Apollo College of Nursing, Chennai, were selected by purposive sampling technique. The instrument used for data collection was Knowledge Questionnaire on Paediatric Basic Life Support (PBLS). It consists of 29 questions on the general expansion of Basic Life Support and Cardiopulmonary Resuscitation, Prerequisites of Basic Life Support, and Knowledge on Paediatric Basic Life Support in which each question has four multiple choices answers, each right answer carrying one mark and no negative scoring. This questionnaire was formed with reference to AHA 2020 (American Heart Association) revised guidelines. Results: After the post-test, in the web-based learning group, 58.8% of the students had an inadequate level of objective performance score, while 41.1% of them had an adequate level of objective performance score. In the blended learning group, 26.5% of the students had an inadequate level of an objective performance score, and 73.4% of the students had an adequate level of an objective performance score. There was an association between the post-test level of knowledge and the demographic variables of undergraduate nursing students undergoing blended learning. The age was significant at a p-value of 0.01, and the performance of BLS before was significant at a p-value of 0.05. The results show that there was a significant positive correlation between knowledge and objective performance score of undergraduate nursing students undergoing web-based learning on paediatric basic life support.

Keywords: basic life support, paediatric basic life support, web-based learning, blended learning

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Authors: Qamar Iqbal

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Companies strive to improve their forecasting methods to predict the fluctuations in customer demand. These fluctuation and variation in demand affect the manufacturing operations and can limit a company’s ability to fulfill customer demand on time. Companies keep the inventory buffer and maintain the stocking levels to reduce the impact of demand variation. A mid-size company deals with thousands of stock keeping units (skus). It is neither easy and nor efficient to control and manage each sku. Inventory classification provides a tool to the management to increase their ability to support customer demand. The paper presents a framework that shows how inventory classification can play a role to increase supply chain responsiveness. A case study will be presented to further elaborate the method both for build-to-order and build-to-forecast manufacturing environments. Results will be compared that will show which manufacturing setting has advantage over another under different circumstances. The outcome of this study is very useful to the management because this will give them an insight on how inventory classification can be used to increase their ability to respond to changing customer needs.

Keywords: inventory classification, supply chain responsiveness, forecast, manufacturing environment

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Authors: Ying Yi Wu

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The study of finite groups is a central topic in algebraic structures, and one of the most fundamental questions in this field is the classification of finite groups up to isomorphism. In this paper, we investigate the cyclic property of groups of prime order, which is a crucial result in the classification of finite abelian groups. We prove the following statement: If p is a prime, then every group G of order p is cyclic. Our proof utilizes the properties of group actions and the class equation, which provide a powerful tool for studying the structure of finite groups. In particular, we first show that any non-identity element of G generates a cyclic subgroup of G. Then, we establish the existence of an element of order p, which implies that G is generated by a single element. Finally, we demonstrate that any two generators of G are conjugate, which shows that G is a cyclic group. Our result has significant implications in the classification of finite groups, as it implies that any group of prime order is isomorphic to the cyclic group of the same order. Moreover, it provides a useful tool for understanding the structure of more complicated finite groups, as any finite abelian group can be decomposed into a direct product of cyclic groups. Our proof technique can also be extended to other areas of group theory, such as the classification of finite p-groups, where p is a prime. Therefore, our work has implications beyond the specific result we prove and can contribute to further research in algebraic structures.

Keywords: group theory, finite groups, cyclic groups, prime order, classification.

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Authors: Marcelino Caetano Noronha, Vosco Pereira, Jose Soares Pinto, Ferdinando Da C. Saores

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One particularly popular social media platform is Youtube. It’s a video-sharing platform where users can submit videos, and other users can like, dislike or comment on the videos. In this study, we conduct a binary classification task on YouTube’s video comments and review from the users regarding the accession process of Timor Leste to become the eleventh member of the Association of South East Asian Nations (ASEAN). We scrape the data directly from the public YouTube video and apply several pre-processing and weighting techniques. Before conducting the classification, we categorized the data into two classes, namely positive and negative. In the classification part, we apply Support Vector Machine (SVM) algorithm. By comparing with Naïve Bayes Algorithm, the experiment showed SVM achieved 84.1% of Accuracy, 94.5% of Precision, and Recall 73.8% simultaneously.

Keywords: classification, YouTube, sentiment analysis, support sector machine

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Authors: Gamze Uslu, Sebnem Baydere, Alper K. Demir

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In this study, data loss tolerance of Support Vector Machines (SVM) based activity recognition model and multi activity classification performance when data are received over a lossy wireless sensor network is examined. Initially, the classification algorithm we use is evaluated in terms of resilience to random data loss with 3D acceleration sensor data for sitting, lying, walking and standing actions. The results show that the proposed classification method can recognize these activities successfully despite high data loss. Secondly, the effect of differentiated quality of service performance on activity recognition success is measured with activity data acquired from a multi hop wireless sensor network, which introduces high data loss. The effect of number of nodes on the reliability and multi activity classification success is demonstrated in simulation environment. To the best of our knowledge, the effect of data loss in a wireless sensor network on activity detection success rate of an SVM based classification algorithm has not been studied before.

Keywords: activity recognition, support vector machines, acceleration sensor, wireless sensor networks, packet loss

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Authors: B. P. G. Figueira, I. C. Pinto, D. Ferro, F. C. M. Zacharias

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The absenteeism at work constitutes in a temporary absence of labor functions resulting from various reasons, bringing damage to production, increasing costs of care and overburdening other workers, has its principal cause due to illness, often due exposure to several risks in the workplace. This study aims to know, identify and analyze the types and causes of absenteeism, such as the frequency at which it occurs by professional category, for employment contract and days not worked in Emergency Care Public in a city in the interior of São Paulo. We conducted exploratory and descriptive study with a quantitative approach, with nursing professionals, after selection of inclusion criteria was reached a universe of 208 subjects, the data collected are for the years from 2010-2013. Research has shown that the professional category of nursing assistant had 88,11% of total absenteeism, absenteeism lasting 1 day was the with the highest frequency, the women were responsible for 74,80% of absenteeism disease. It was concluded that absenteeism shall be monitored to plan control actions, establishing better political for the management of human resources, because it can be an aggravating factor in the quality of care.

Keywords: absenteeism; nursing; emergency medical services, human resource

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Authors: Ktiri Fouad

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Objectives: The present study examined how the polyvalent nursing students of the Higher Institute of Nursing Professions and Health Techniques (ISPITS-Rabat-Morocco) conceived the term "feeling stressed.” We checked whether they were referring to a specific type of sensation (emotional, mental, physical) or both or all of them when they said they were stressed at the time they felt it. Materials and methods: A quantitative cross-sectional study was conducted among students of the three years of polyvalent nursing courses. Using a 7-Likert scale, the students were asked to assess their states of stress and the emotional, mental and physical sensations they were experiencing before and after carrying out a mental arithmetic task. An ordinal logistic regression method was used to investigate the association between the states of stress and the 3 types of sensations. Results: 222 polyvalent nursing students out of 307 were included in the experience. Their increased perceived states of stress after carrying out the mental task were found to be significantly associated with emotional distress and mental fatigue and not with physical tiredness. The mental sensation (mental fatigue) was found to have more effects in predicting the likelihood of feeling stressed. In addition, the lower the intensity of emotional or mental sensation, the more likely the students were to experience stress, given that one of both sensations is held constant, whatever the intensity of the physical sensation. We conclude that the polyvalent nursing students refer to mental fatigue and emotional distress and not to physical tiredness when they say they felt stressed, the mental fatigue having more effects. The implications of the study are discussed.

Keywords: feeling stressed”, emotional sensation, mental sensation, physical sensation

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Authors: M. Aghili, S. Tabarestani, C. Freytes, M. Shojaie, M. Cabrerizo, A. Barreto, N. Rishe, R. E. Curiel, D. Loewenstein, R. Duara, M. Adjouadi

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A major challenge in medical studies, especially those that are longitudinal, is the problem of missing measurements which hinders the effective application of many machine learning algorithms. Furthermore, recent Alzheimer's Disease studies have focused on the delineation of Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI) from cognitively normal controls (CN) which is essential for developing effective and early treatment methods. To address the aforementioned challenges, this paper explores the potential of using the eXtreme Gradient Boosting (XGBoost) algorithm in handling missing values in multiclass classification. We seek a generalized classification scheme where all prodromal stages of the disease are considered simultaneously in the classification and decision-making processes. Given the large number of subjects (1631) included in this study and in the presence of almost 28% missing values, we investigated the performance of XGBoost on the classification of the four classes of AD, NC, EMCI, and LMCI. Using 10-fold cross validation technique, XGBoost is shown to outperform other state-of-the-art classification algorithms by 3% in terms of accuracy and F-score. Our model achieved an accuracy of 80.52%, a precision of 80.62% and recall of 80.51%, supporting the more natural and promising multiclass classification.

Keywords: eXtreme gradient boosting, missing data, Alzheimer disease, early mild cognitive impairment, late mild cognitive impair, multiclass classification, ADNI, support vector machine, random forest

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Authors: Ellen Dahl Gundersen, Berit Johannessen

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Introduction: Dementia and depression are common mental disorders of nursing home residents. The care of these residents consists of providing both physical, social and mental care. Too often, the physical needs are given priority, and municipal health services are urged to focus more on the patients mental and social needs. The presence of dogs may have positive impact on the mental health of nursing home residents by improving mood, social interaction and enjoyment of the visits. The voluntary organization Red Cross, has given priority to this subject by training and certifying dogs and owners (equipages), committed for regular visits at local nursing homes. Focus of this study: How do the dog owners and employees experience the presence of a dog equipage concerning the mental health of nursing home residents? Method: Individual interviews with 8-10 certified dog owners who are volunteers from Red Cross, contributing with regular visits at local nursing homes. Focus group interviews with 10 employees working in two different nursing homes. Preliminary results: Five to seven residents and one or two employees attended weekly dog equipage visits during a period of six months. The presence of an equipage seems to have made the residents calm and more social orientated with a lighter mood and better verbal expression. Some of the residents with dementia remembered the name of the dog from one week to another. The informants also reported positive outcome for the residents by their opportunity to give and get close through physical contact with a dog. Further, the presence of an equipage affected the atmosphere at the nursing home positively by promoting joy and initiating conversations about dogs. A conscious approach by the dog owners towards the residents seems to be of significance to this matter. The positive attitude and support from employees also seem to be of crucial importance for the maintenance of these visits. Conclusion: The presence of trained dog equipages in nursing homes seems to have had an overall positive impact on the mental health of residents. A conscious approach from the dog owners as well as positive support from employees seems to have a crucial impact on the success and maintenance of the visits. These findings correspond well to former research and can thereby give implications for more extended use of dogs as a mental health promoting initiative towards geriatric consumers of municipal health care services. Further research through larger studies is needed.

Keywords: animal assisted intervention, geriatric mental health, nursing home, resident

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Authors: Imen Elleuch, Wael Ouarda, Gargouri Bilel

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We proposes in this paper a Deep Learning based approach to classify text in order to enrich an Arabic ontology based on the objects classes of Gaston Gross. Those object classes are defined by taking into account the syntactic and semantic features of the treated language. Thus, our proposed approach is a hybrid one. In fact, it is based on the one hand on the object classes that represents a knowledge based-approach on classification of text and in the other hand it uses the deep learning approach that use the word embedding-based-approach to classify text. We have applied our proposed approach on a corpus constructed from an Arabic dictionary. The obtained semantic classification of text will enrich the Arabic objects classes ontology. In fact, new classes can be added to the ontology or an expansion of the features that characterizes each object class can be updated. The obtained results are compared to a similar work that treats the same object with a classical linguistic approach for the semantic classification of text. This comparison highlight our hybrid proposed approach that can be ameliorated by broaden the dataset used in the deep learning process.

Keywords: deep-learning approach, object-classes, semantic classification, Arabic

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Authors: Colin Smith, Linsey S Passarella

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Due to the proliferation and decentralized nature of academic publication, no widely accepted scheme exists for organizing papers by their scientific field of study (FoS) to the author’s best knowledge. While many academic journals require author provided keywords for papers, these keywords range wildly in scope and are not consistent across papers, journals, or field domains, necessitating alternative approaches to paper classification. Past attempts to perform field-of-study (FoS) classification on scientific texts have largely used a-hierarchical FoS schemas or ignored the schema’s inherently hierarchical structure, e.g. by compressing the structure into a single layer for multi-label classification. In this paper, we introduce an application of a Layered Neural Network (LNN) to the problem of performing supervised hierarchical classification of scientific fields of study (FoS) on research papers. In this approach, paper embeddings from a pretrained language model are fed into a top-down LNN. Beginning with a single neural network (NN) for the highest layer of the class hierarchy, each node uses a separate local NN to classify the subsequent subfield child node(s) for an input embedding of concatenated paper titles and abstracts. We compare our LNN-FOS method to other recent machine learning methods using the Microsoft Academic Graph (MAG) FoS hierarchy and find that the LNN-FOS offers increased classification accuracy at each FoS hierarchical level.

Keywords: hierarchical classification, layer neural network, scientific field of study, scientific taxonomy

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Authors: Sannikumar Patel, Brian Nolan, Markus Hofmann, Philip Owende, Kunjan Patel

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Sentiment analysis and opinion mining have become emerging topics of research in recent years but most of the work is focused on data in the English language. A comprehensive research and analysis are essential which considers multiple languages, machine translation techniques, and different classifiers. This paper presents, a comparative analysis of different approaches for multilingual sentiment analysis. These approaches are divided into two parts: one using classification of text without language translation and second using the translation of testing data to a target language, such as English, before classification. The presented research and results are useful for understanding whether machine translation should be used for multilingual sentiment analysis or building language specific sentiment classification systems is a better approach. The effects of language translation techniques, features, and accuracy of various classifiers for multilingual sentiment analysis is also discussed in this study.

Keywords: cross-language analysis, machine learning, machine translation, sentiment analysis

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Authors: Inna R. Edara, Haw-Lin Wu

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Positive psychology investigates human strengths and virtues and promotes well-being. Relying on this assumption, positive interventions have been continuously designed to build pleasure and happiness, joy and contentment, engagement and meaning, hope and optimism, satisfaction and gratitude, spirituality, and various other positive measures of well-being. In line with this model of positive psychology and interventions, this study investigated certain measures of well-being in a group of 45 students enrolled in an 18-week positive psychology course and simultaneously engaged in service-oriented interventions that they chose for themselves based on the course content and individual interests. Students’ well-being was measured at the beginning and end of the course. The well-being indicators included positive automatic thoughts, optimism and hope, satisfaction with life, and spirituality. A paired-samples t-test conducted to evaluate the impact of class content and service-oriented interventions on students’ scores of well-being indicators indicated statistically significant increase from pre-class to post-class scores. There were also significant gender differences in post-course well-being scores, with females having higher levels of well-being than males. A two-way between groups analysis of variance indicated a significant interaction effect of age by gender on the post-course well-being scores, with females in the age group of 56-65 having the highest scores of well-being in comparison to the males in the same age group. Regression analyses indicated that positive automatic thought significantly predicted hope and satisfaction with life in the pre-course analysis. In the post-course regression analysis, spiritual transcendence made a significant contribution to optimism, and positive automatic thought made a significant contribution to both hope and satisfaction with life. Finally, a significant test between pre-course and post-course regression coefficients indicated that the regression coefficients at pre-course were significantly different from post-course coefficients, suggesting that the positive psychology course and the interventions were helpful in raising the levels of well-being. The overall results suggest a substantial increase in the participants’ well-being scores after engaging in the positive-oriented interventions, implying a need for designing more positive interventions in education to promote well-being.  

Keywords: hope, optimism, positive automatic thoughts, satisfaction with life, spirituality, well-being

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Authors: Md Kamrul Hassan

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Adaptation interventions are the common response to manage the vulnerabilities of climate change. The nature of adaptation intervention depends on the degree of vulnerability and the capacity of a society. The coping interventions can take the form of hard adaptation – utilising technologies and capital goods like dykes, embankments, seawalls, and/or soft adaptation – engaging knowledge and information sharing, capacity building, policy and strategy development, and innovation. Hard adaptation is quite capital intensive but provides immediate relief from climate change vulnerabilities. This type of adaptation is not real development, as the investment for the adaptation cannot improve the performance – just maintain the status quo of a social or ecological system, and often lead to maladaptation in the long-term. Maladaptation creates a two-way loss for a society – interventions bring further vulnerability on top of the existing vulnerability and investment for getting rid of the consequence of interventions. Hard adaptation is popular to the vulnerable groups, but it focuses so much on the immediate solution and often ignores the environmental issues and future risks of climate change. On the other hand, soft adaptation is education oriented where vulnerable groups learn how to live with climate change impacts. Soft adaptation interventions build the capacity of vulnerable groups through training, innovation, and support, which might enhance the resilience of a system. In consideration of long-term sustainability, soft adaptation can contribute more to resilience than hard adaptation. Taking a developing society as the study context, this study aims to investigate and understand the effectiveness of the adaptation interventions of the coastal community of Sundarbans mangrove forest in Bangladesh. Applying semi-structured interviews with a range of Sundarbans stakeholders including community residents, tourism demand-supply side stakeholders, and conservation and management agencies (e.g., Government, NGOs and international agencies) and document analysis, this paper reports several key insights regarding climate change adaptation. Firstly, while adaptation interventions may offer a short-term to medium-term solution to climate change vulnerabilities, interventions need to be revised for long-term sustainability. Secondly, soft adaptation offers advantages in terms of resilience in a rapidly changing environment, as it is flexible and dynamic. Thirdly, there is a challenge to communicate to educate vulnerable groups to understand more about the future effects of hard adaptation interventions (and the potential for maladaptation). Fourthly, hard adaptation can be used if the interventions do not degrade the environmental balance and if the investment of interventions does not exceed the economic benefit of the interventions. Overall, the goal of an adaptation intervention should be to enhance the resilience of a social or ecological system so that the system can with stand present vulnerabilities and future risks. In order to be sustainable, adaptation interventions should be designed in such way that those can address vulnerabilities and risks of climate change in a long-term timeframe.

Keywords: adaptation, climate change, maladaptation, resilience, Sundarbans, sustainability, vulnerability

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Authors: Ya-Ping Chang

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Starting from the early stages of pregnancy, parents begin to form hopes and dreams about the future of their child. They will think about the appearance and personality of their child and may even develop many expectations. The patient in this study experienced a successful pregnancy following multiple attempts at artificial insemination. However, due to arrested embryonic development, and based on the physician’s evaluation, a caesarean section was performed at week 25. However, the baby suffered from infections and subsequently died from multiple organ failures. This study collected and analyzed objective and subjective data through observation, interviews, recording, and interactions with the patient. The following nursing issues of the patient were identified: anxiety, anticipatory grief, and adjustment disorder. The psychology of caring as proposed in Watson’s theory was applied to address these nursing issues. Comprehensive and continuous care was provided to the patient on the basis of mutual trust and individual nursing guidelines in order to alleviate the patient’s anxiety, help her to cope with grief, and prepare her for the eventual death of her child. The author helped the patient to say goodbye to her child and accept the child’s death calmly, such that she had no regrets about the experience. This nursing experience may serve as a reference to nurses managing similar cases in the future.

Keywords: dying baby, mother, grief, Watson’s theory

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Authors: Yafa Haron, Hanna Adami

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Background: World Health Organization emphasizes the contribution of nurses in planning and implementing health policies and reforms. Aim: To evaluate nursing students’ attitudes towards nurses’ involvement in health policy issues. Methods: Mixed-methods; qualitative and quantitative – a descriptive study. Participants - nursing students who were enrolled in their last year in the undergraduate program (BSN). Qualitative data included two open-ended questions: What is health policy and what is the importance of studying health policy, and 18 statements on the Likert Scale range 1-5. Results: Qualitativeanalysisrevealed that the majority of students defined health policy as a set of rules and regulations that defined procedures, borders, and proper conduct. 73% of students responded that nurses should be active in policymaking, but only 22% thought that nurses were currently involved in political issues. 28% thought that nurses do not have the knowledge and the time needed (60%) for political activity. 77% thought that the work environment did not encourage nurses to be politically active. Nursing students are aware of the importance towards nurses’ involvement in health policy issues, however, they do not have role models based on their low evaluation regarding nurses’ involvement in the health policy decision making process at the local or national level. Conclusions: Results emphasize the importance and the need of implementation the recommendation to include “advance policy changes” as core competency in nursing education and practice.

Keywords: health policy, nursing education, health systems, student perceptions

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Authors: Menna Brown, Tania Domun

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Digital health interventions are effective across a wide array of health conditions spanning physical health, lifestyle behaviour change, and mental health and wellbeing; furthermore, they are rapidly increasing in volume within both the academic literature and society as commercial apps continue to proliferate the digital health market. However, adherence and engagement to digital health interventions remains problematic. Technology-based personalised and tailored reminder strategies can support engagement to digital health interventions. Interventions which support individuals’ mental health and wellbeing are of critical importance in the wake if the COVID-19 pandemic. Student and young person’s mental health has been negatively affected and digital resources continue to offer cost effective means to address wellbeing at a population level. Develop a databank of digital co-produced tailored messages to support engagement to a range of digital health interventions including those focused on mental health and wellbeing, and lifestyle behaviour change. Qualitative research design. Participants discussed their views of health and wellbeing, engagement and adherence to digital health interventions focused around a 12-week wellbeing intervention via a series of focus group discussions. They worked together to co-create content following a participatory design approach. Three focus group discussions were facilitated with (n=15) undergraduate students at one Welsh university to provide an empirically derived, co-produced, databank of (n=145) tailored messages. Messages were explored and categorised thematically, and the following ten themes emerged: Autonomy, Recognition, Guidance, Community, Acceptance, Responsibility, Encouragement, Compassion, Impact and Ease. The findings provide empirically derived, co-produced tailored messages. These have been made available for use, via ‘ACTivate your wellbeing’ a digital, automated, 12-week health and wellbeing intervention programme, based on acceptance and commitment therapy (ACT). The purpose of which is to support future research to evaluate the impact of thematically categorised tailored messages on engagement and adherence to digital health interventions.

Keywords: digital health, engagement, wellbeing, participatory design, positive psychology, co-production

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Authors: Nurul Aini Abdul Wahab, Nor Syamim Halidin, Sayidatina Aisah Masnan, Nur Izzati Romli

Abstract:

There are many applications of principle component method for reducing the large set of variables in various fields. Fisher’s Discriminant function is also a popular tool for classification. In this research, the researcher focuses on studying the performance of Principle Component-Fisher’s Discriminant function in helping to classify rice kernels to their defined classes. The data were collected on the smells or odour of the rice kernel using odour-detection sensor, Cyranose. 32 variables were captured by this electronic nose (e-nose). The objective of this research is to measure how well a combination model, between principle component and linear discriminant, to be as a classification model. Principle component method was used to reduce all 32 variables to a smaller and manageable set of components. Then, the reduced components were used to develop the Fisher’s Discriminant function. In this research, there are 4 defined classes of rice kernel which are Aromatic, Brown, Ordinary and Others. Based on the output from principle component method, the 32 variables were reduced to only 2 components. Based on the output of classification table from the discriminant analysis, 40.76% from the total observations were correctly classified into their classes by the PC-Discriminant function. Indirectly, it gives an idea that the classification model developed has committed to more than 50% of misclassifying the observations. As a conclusion, the Fisher’s Discriminant function that was built on a 2-component from PCA (2-PC-D) is not satisfying to classify the rice kernels into its defined classes.

Keywords: classification model, discriminant function, principle component analysis, variable reduction

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Authors: Jui Chen Wu, Ming Yi Hsu

Abstract:

Background and Purpose: Shift work of nursing staff is inevitable in hospital to provide continuing medical care. However, shift work is considered as a health hazard that may cause physical and psychological problems. Serious workplace fatigue of nursing shift work might impact on family, social and work life, moreover, causes serious reduction of quality of medical care, or even malpractice. This study aims to explore relationships among nursing staff’s shift, workplace fatigue and quality of working life. Method: Structured questionnaires were used in this study to explore relationships among shift work, workplace fatigue and quality of working life in nursing staffs. We recruited 590 nursing staffs in different Community Teaching hospitals in Taiwan. Data analysed by descriptive statistics, single sample t-test, single factor analysis, Pearson correlation coefficient and hierarchical regression, etc. Results: The overall workplace fatigue score is 50.59 points. In further analysis, the score of personal burnout, work-related burnout, over-commitment and client-related burnout are 57.86, 53.83, 45.95 and 44.71. The basic attributes of nursing staff are significantly different from those of workplace fatigue with different ages, licenses, sleeping quality, self-conscious health status, number of care patients of chronic diseases and number of care people in the obstetric ward. The shift variables revealed no significant influence on workplace fatigue during the hierarchical regression analysis. About the analysis on nursing staff’s basic attributes and shift on the quality of working life, descriptive results show that the overall quality of working life of nursing staff is 3.23 points. Comparing the average score of the six aspects, the ranked average score are 3.47 (SD= .43) in interrelationship, 3.40 (SD= .46) in self-actualisation, 3.30 (SD= .40) in self-efficacy, 3.15 (SD= .38) in vocational concept, 3.07 (SD= .37) in work aspects, and 3.02 (SD= .56) in organization aspects. The basic attributes of nursing staff are significantly different from quality of working life in different marriage situations, education level, years of nursing work, occupation area, sleep quality, self-conscious health status and number of care in medical ward. There are significant differences between shift mode and shift rate with the quality of working life. The results of the hierarchical regression analysis reveal that one of the shifts variables 'shift mode' which does affect staff’s quality of working life. The workplace fatigue is negatively correlated with the quality of working life, and the over-commitment in the workplace fatigue is positively related to the vocational concept of the quality of working life. According to the regression analysis of nursing staff’s basic attributes, shift mode, workplace fatigue and quality of working life related shift, the results show that the workplace fatigue has a significant impact on nursing staff’s quality of working life. Conclusion: According to our study, shift work is correlated with workplace fatigue in nursing staffs. This results work as important reference for human resources management in hospitals to establishing a more positive and healthy work arrangement policy.

Keywords: nursing staff, shift, workplace fatigue, quality of working life

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Authors: Mohamed R. Mhereeg

Abstract:

The paper discusses the design of a .NET Windows Service based agent system called MACS (Multi-Agent Classification System). MACS is a system aims to accurately classify spread-sheet developers competency over a network. It is designed to automatically and autonomously monitor spread-sheet users and gather their development activities based on the utilization of the software Multi-Agent Technology (MAS). This is accomplished in such a way that makes management capable to efficiently allow for precise tailor training activities for future spread-sheet development. The monitoring agents of MACS are intended to be distributed over the WWW in order to satisfy the monitoring and classification of the multiple developer aspect. The Prometheus methodology is used for the design of the agents of MACS. Prometheus has been used to undertake this phase of the system design because it is developed specifically for specifying and designing agent-oriented systems. Additionally, Prometheus specifies also the communication needed between the agents in order to coordinate to achieve their delegated tasks.

Keywords: classification, design, MACS, MAS, prometheus

Procedia PDF Downloads 373

Authors: Susan Ka Yee Chow

Abstract:

Effective clinical preceptorship is affecting students’ competence and fostering their growth in applying theoretical knowledge and skills in clinical settings. Any difference between the expected and actual learning experience will reduce nursing students’ interest in clinical practices and having a negative consequence with their clinical performance. This cross-sectional study is an attempt to compare the differences between preferred and actual preceptorship experience of final year nursing students, and to examine the relationship between the actual preceptorship experience and perceived clinical competence of the students in a tertiary institution. Participants of the study were final year bachelor nursing students of a self-financing tertiary institution in Hong Kong. The instruments used to measure the effectiveness of clinical preceptorship was developed by the participating institution. The scale consisted of five items in a 5-point likert scale. The questions including goals development, critical thinking, learning objectives, asking questions and providing feedback to students. The “Clinical Competence Questionnaire” by Liou & Cheng (2014) was used to examine students’ perceived clinical competences. The scale consisted of 47 items categorized into four domains, namely nursing professional behaviours; skill competence: general performance; skill competence: core nursing skills and skill competence: advanced nursing skills. There were 193 questionnaires returned with a response rate of 89%. The paired t-test was used to compare the differences between preferred and actual preceptorship experiences of students. The results showed significant differences (p<0.001) for the five questions. The mean for the preferred scores is higher than the actual scores resulting statistically significance. The maximum mean difference was accepted goal and the highest mean different was giving feedback. The Pearson Correlation Coefficient was used to examine the relationship. The results showed moderate correlations between nursing professional behaviours with asking questions and providing feedback. Providing useful feedback to students is having moderate correlations with all domains of the Clinical Competence Questionnaire (r=0.269 – 0.345). It is concluded that nursing students do not have a positive perception of the clinical preceptorship. Their perceptions are significantly different from their expected preceptorship. If students were given more opportunities to ask questions in a pedagogical atmosphere, their perceived clinical competence and learning outcomes could be improved as a result.

Keywords: clinical preceptor, clinical competence, clinical practicum, nursing students

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