Search results for: breathing training program

Authors: A. Giannakopoulos, S. B. Buckley

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Problem solving in any field is recognised as a prerequisite for any advancement in knowledge. For example in South Africa it is one of the seven critical outcomes of education together with critical thinking. As a systematic way to problem solving was initiated in mathematics by the great mathematician George Polya (the father of problem solving), more detailed and comprehensive ways in problem solving have been developed. This paper is based on the findings by the author and subsequent recommendations for further research in problem solving and critical thinking. Although the study was done in mathematics, there is no doubt by now in almost anyone’s mind that mathematics is involved to a greater or a lesser extent in all fields, from symbols, to variables, to equations, to logic, to critical thinking. Therefore it stands to reason that mathematical principles and learning cannot be divorced from any field. In management of knowledge situations, the types of problems are similar to mathematics problems varying from simple to analogical to complex; from well-structured to ill-structured problems. While simple problems could be solved by employees by adhering to prescribed sequential steps (the process), analogical and complex problems cannot be proceduralised and that diminishes the capacity of the organisation of knowledge creation and innovation. The low efficiency in some organisations and the low pass rates in mathematics prompted the author to view problem solving as a product. The authors argue that using mathematical approaches to knowledge management problem solving and treating problem solving as a product will empower the employee through further training to tackle analogical and complex problems. The question the authors asked was: If it is true that problem solving and critical thinking are indeed basic skills necessary for advancement of knowledge why is there so little literature of knowledge management (KM) about them and how they are connected and advance KM?This paper concludes with a conceptual model which is based on general accepted principles of knowledge acquisition (developing a learning organisation), knowledge creation, sharing, disseminating and storing thereof, the five pillars of knowledge management (KM). This model, also expands on Gray’s framework on KM practices and problem solving and opens the doors to a new approach to training employees in general and domain specific areas problems which can be adapted in any type of organisation.

Keywords: critical thinking, knowledge management, mathematics, problem solving

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Authors: Helen Coronel, Will Brewer, Peggy Bergeron, Clarissa Hall, Victoria Casson

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Simulation-based training provides the nurse practitioner (NP) student with a safe and controlled environment in which they can practice a real-life scenario. This type of learning fosters critical thinking skills essential to practice. The expectation of this study was that students would have an increase in their competency and confidence after performing the simulation. Approximately 8.4% of Americans suffer from depression. The state of Alabama is ranked 47th out of 50 for access to mental health care. As a result of this significant shortage of mental health providers, primary care providers are frequently put in the position of screening for and treating mental health conditions, such as depression. Family nurse practitioners are often utilized as primary care providers, making their ability to assess, diagnose and treat these disorders a necessary skill. The expected outcome of this simulation is an increase in confidence, competency and the empowerment of the nurse practitioner student’s ability to assess, diagnose and treat a common mood disorder they may encounter in practice. The Kirkpatrick Module was applied for this study. A non-experimental design using descriptive statistical analysis was utilized. The simulation was based on a common psychiatric mood disorder frequently observed in primary care and mental health clinics. Students were asked to watch a voiceover power point presentation prior to their on-campus simulation. The presentation included training on the assessment, diagnosis, and treatment of a patient with depression. Prior to the simulation, the students completed a pre-test, then participated in the simulation, and completed a post-test when done. Apple iPads were utilized to access a simulated health record. Major findings of the study support an increase in students’ competency and confidence when assessing, diagnosing, and treating an adult patient with depression.

Keywords: advanced practice, nurse practitioner, simulation, primary care, depression

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Authors: Pedram Saadati, Zahra Nazari

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Today, the role and importance of financial technology businesses in Iran have increased significantly. Although, in Iran, there is no Islamic or non-Islamic personal financial planning field of study in the universities or educational centers, the profession of personal financial planning is not defined, and there is no software introduced in this regard for advisors or consumers. The largest sales network of financial services in Iran belongs to the insurance industry, and there is an untapped market for international companies in Iran that can contribute to 130 thousand representatives in the insurance industry and 28 million families by providing training and personal financial advisory software. To the best of the author's knowledge, despite the lack of previous internal studies in this field, the present study investigates the level of readiness of the salesforce of the insurance industry to accept this career and its technology. The statistical population of the research is made up of managers, insurance sales representatives, assistants and heads of sales departments of insurance companies. An 18-minute video was prepared that introduced and taught the job of Islamic personal financial planning and explained its difference from its non-Islamic model. This video was provided to the respondents. The data collection tool was a research-made questionnaire. To investigate the factors affecting technology acceptance and job change, independent T descriptive statistics and Pearson correlation were used, and Friedman's test was used to rank the effective factors. The results indicate the mental perception and very positive attitude of the insurance industry activists towards the usefulness of this job and its technology, and the studied sample confirmed the intention of training in this knowledge. Based on research results, the change in the customer's attitude towards the insurance advisor and the possibility of increasing income are considered as the reasons for accepting. However, Restrictions on using investment opportunities due to Islamic financial services laws and the uncertainty of the position of the central insurance in this regard are considered as the most important obstacles.

Keywords: fintech, insurance, personal financial planning, wealth management

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Authors: H. C. Chen, S. W. C. Chan, K. Cheng, A. Vathsala, H. K. Sran, H. He

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Background: End-stage renal disease (ESRD) is the last stage of chronic kidney disease. The numbers of patients with ESRD have increased worldwide due to the growing number of aging, diabetes and hypertension populations. Patients with ESRD suffer from physical illness and psychological distress due to complex treatment regimens, which often affect the patients’ social and psychological functioning. As a result, the patients may fail to perform daily self-care and self-management, and consequently experience worsening conditions. Aims: The study aims to examine the effectiveness of a self-efficacy psychoeducational programme on primary outcome (self-efficacy) and secondary outcomes (psychological wellbeing, treatment adherence, and quality of life) in patients with ESRD and haemodialysis in Singapore. Methodology: A randomised controlled, two-group pretest and repeated posttests design will be carried out. A total of 154 participants (n=154) will be recruited. The participants in the control group will receive a routine treatment. The participants in the intervention group will receive a self-efficacy psychoeducational programme in addition to the routine treatment. The programme is a two-session of educational intervention in a week. A booklet, two consecutive sessions of face-to-face individual education, and an abdominal breathing exercise are adopted in the programme. Outcome measurements include Dialysis Specific Self-efficacy Scale, Kidney Disease Quality of Life- 36 Hospital Anxiety and Depression Scale, Renal Adherence Attitudes Questionnaire and Renal Adherence Behaviour Questionnaire. The questionnaires will be used to measure at baseline, 1- and 3- and 6-month follow-up periods. Process evaluation will be conducted with a semi-structured face to face interview. Quantitative data will be analysed using SPSS21.0 software. Qualitative data will be analysed by content analysis. Significance of the study: This study will identify a clinically useful and potentially effective approach to help patients with end-stage renal disease and haemodialysis by enhancing their self-efficacy in self-care behaviour, and therefore improving their psychological well-being, treatment adherence and quality of life. This study will provide information to develop clinical guidelines to improve patients’ disease self-management and to enhance health-related outcomes and it will help reducing disease burden.

Keywords: end-stage renal disease (ESRD), haemodialysis, psychoeducation, self-efficacy

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Authors: Afzal Hussain, Mohammad A. Altamimi, Syed Sarim Imam, Mudassar Shahid, Osamah Abdulrahman Alnemer

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Exhaustive application of sulfamethoxazole (SUX) became as a global threat for human health due to water contamination through diverse sources. The addressed combined application of Hansen solubility (HSPiP software) parameters and Quality by Design tool for developing various green nanoemulsions. HSPiP program assisted to screen suitable excipients based on Hansen solubility parameters and experimental solubility data. Various green nanoemulsions were prepared and characterized for globular size, size distribution, zeta potential, and removal efficiency. Design Expert (DoE) software further helped to identify critical factors responsible to have direct impact on percent removal efficiency, size, and viscosity. Morphological investigation was visualized under transmission electron microscopy (TEM). Finally, the treated was studied to negate the presence of the tested drug employing ICP-OES (inductively coupled plasma optical emission microscopy) technique and HPLC (high performance liquid chromatography). Results showed that HSPiP predicted biocompatible lipid, safe surfactant (lecithin), and propylene glycol (PG). Experimental solubility of the drug in the predicted excipients were quite convincing and vindicated. Various green nanoemulsions were fabricated, and these were evaluated for in vitro findings. Globular size (100-300 nm), PDI (0.1-0.5), zeta potential (~ 25 mV), and removal efficiency (%RE = 70-98%) were found to be in acceptable range for deciding input factors with level in DoE. Experimental design tool assisted to identify the most critical variables controlling %RE and optimized content of nanoemulsion under set constraints. Dispersion time was varied from 5-30 min. Finally, ICP-OES and HPLC techniques corroborated the absence of SUX in the treated water. Thus, the strategy is simple, economic, selective, and efficient.

Keywords: quality by design, sulfamethoxazole, green nanoemulsion, water treatment, icp-oes, hansen program (hspip software

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Authors: Irene Yi

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Machine learning, natural language processing, and neural network models of language are becoming more and more prevalent in the fields of technology and linguistics today. Training data for machines are at best, large corpora of human literature and at worst, a reflection of the ugliness in society. Computational linguistics is a growing field dealing with such issues of data collection for technological development. Machines have been trained on millions of human books, only to find that in the course of human history, derogatory and sexist adjectives are used significantly more frequently when describing females in history and literature than when describing males. This is extremely problematic, both as training data, and as the outcome of natural language processing. As machines start to handle more responsibilities, it is crucial to ensure that they do not take with them historical sexist and misogynistic notions. This paper gathers data and algorithms from neural network models of language having to deal with syntax, semantics, sociolinguistics, and text classification. Computational analysis on such linguistic data is used to find patterns of misogyny. Results are significant in showing the existing intentional and unintentional misogynistic notions used to train machines, as well as in developing better technologies that take into account the semantics and syntax of text to be more mindful and reflect gender equality. Further, this paper deals with the idea of non-binary gender pronouns and how machines can process these pronouns correctly, given its semantic and syntactic context. This paper also delves into the implications of gendered grammar and its effect, cross-linguistically, on natural language processing. Languages such as French or Spanish not only have rigid gendered grammar rules, but also historically patriarchal societies. The progression of society comes hand in hand with not only its language, but how machines process those natural languages. These ideas are all extremely vital to the development of natural language models in technology, and they must be taken into account immediately.

Keywords: computational analysis, gendered grammar, misogynistic language, neural networks

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Authors: Javier Muro, Anja Linstadter, Florian Manner, Lisa Schwarz, Stephan Wollauer, Paul Magdon, Gohar Ghazaryan, Olena Dubovyk

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Monitoring the trade-off between biomass production and biodiversity in grasslands is critical to evaluate the effects of management practices across environmental gradients. New generations of remote sensing sensors and machine learning approaches can model grasslands’ characteristics with varying accuracies. However, studies often fail to cover a sufficiently broad range of environmental conditions, and evidence suggests that prediction models might be case specific. In this study, biomass production and biodiversity indices (species richness and Fishers’ α) are modeled in 150 grassland plots for three sites across Germany. These sites represent a North-South gradient and are characterized by distinct soil types, topographic properties, climatic conditions, and management intensities. Predictors used are derived from Sentinel-1 & 2 and a set of topoedaphic variables. The transferability of the models is tested by training and validating at different sites. The performance of feed-forward deep neural networks (DNN) is compared to a random forest algorithm. While biomass predictions across gradients and sites were acceptable (r2 0.5), predictions of biodiversity indices were poor (r2 0.14). DNN showed higher generalization capacity than random forest when predicting biomass across gradients and sites (relative root mean squared error of 0.5 for DNN vs. 0.85 for random forest). DNN also achieved high performance when using the Sentinel-2 surface reflectance data rather than different combinations of spectral indices, Sentinel-1 data, or topoedaphic variables, simplifying dimensionality. This study demonstrates the necessity of training biomass and biodiversity models using a broad range of environmental conditions and ensuring spatial independence to have realistic and transferable models where plot level information can be upscaled to landscape scale.

Keywords: ecosystem services, grassland management, machine learning, remote sensing

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Authors: Godfrey Muiya Mukala

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This research looks at an array of fields in Sub-Saharan Africa comprising agriculture, food enterprises, medicine, organisms genetically modified, microbiology, and nanotechnology that can be gained from biotechnological research and development. Findings into dangerous organisms, mainly bacterial germs, rickettsia, fungi, parasites, or organisms that are genetically engineered, have immensely posed questions attributed to the biological danger they bring forth to human beings and the environment because of their uncertainties. In addition, the recurrence of previously managed diseases or the inception of new diseases are connected to biosafety challenges, especially in rural set-ups in low and middle-income countries. Notably, biotechnology laboratories are required to adopt biosafety measures to protect their workforce, community, environment, and ecosystem from unforeseen materials and organisms. Sensitization and inclusion of educational frameworks for laboratory workers are essential to acquiring a solid knowledge of harmful biological agents. This is in addition to human pathogenicity, susceptibility, and epidemiology to the biological data used in research and development. This article reviews and analyzes research intending to identify the proper implementation of universally accepted practices in laboratory safety and biological hazards. This research identifies ideal microbiological methods, adequate containment equipment, sufficient resources, safety barriers, specific training, and education of the laboratory workforce to decrease and contain biological hazards. Subsequently, knowledge of standardized microbiological techniques and processes, in addition to the employment of containment facilities, protective barriers, and equipment, is far-reaching in preventing occupational infections. Similarly, reduction of risks and prevention may be attained by training, education, and research on biohazards, pathogenicity, and epidemiology of the relevant microorganisms. In this technique, medical professionals in rural setups may adopt the knowledge acquired from the past to project possible concerns in the future.

Keywords: sub-saharan africa, biotechnology, laboratory, infections, health

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Authors: Akeel A. Shah, Tong Zhang

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Computational approaches to learning the properties of materials are commonplace, motivated by the need to screen or design materials for a given application, e.g., semiconductors and energy storage. Experimental approaches can be both time consuming and costly. Unfortunately, computational approaches such as ab-initio electronic structure calculations and classical or ab-initio molecular dynamics are themselves can be too slow for the rapid evaluation of materials, often involving thousands to hundreds of thousands of candidates. Machine learning assisted approaches have been developed to overcome the time limitations of purely physics-based approaches. These approaches, on the other hand, require large volumes of data for training (hundreds of thousands on many standard data sets such as QM7b). This means that they are limited by how quickly such a large data set of physics-based simulations can be established. At high fidelity, such as configuration interaction, composite methods such as G4, and coupled cluster theory, gathering such a large data set can become infeasible, which can compromise the accuracy of the predictions - many applications require high accuracy, for example band structures and energy levels in semiconductor materials and the energetics of charge transfer in energy storage materials. In order to circumvent this problem, multi-fidelity approaches can be adopted, for example the Δ-ML method, which learns a high-fidelity output from a low-fidelity result such as Hartree-Fock or density functional theory (DFT). The general strategy is to learn a map between the low and high fidelity outputs, so that the high-fidelity output is obtained a simple sum of the physics-based low-fidelity and correction, Although this requires a low-fidelity calculation, it typically requires far fewer high-fidelity results to learn the correction map, and furthermore, the low-fidelity result, such as Hartree-Fock or semi-empirical ZINDO, is typically quick to obtain, For high-fidelity outputs the result can be an order of magnitude or more in speed up. In this work, a new multi-fidelity approach is developed, based on a graph convolutional network (GCN) combined with semi-supervised learning. The GCN allows for the material or molecule to be represented as a graph, which is known to improve accuracy, for example SchNet and MEGNET. The graph incorporates information regarding the numbers of, types and properties of atoms; the types of bonds; and bond angles. They key to the accuracy in multi-fidelity methods, however, is the incorporation of low-fidelity output to learn the high-fidelity equivalent, in this case by learning their difference. Semi-supervised learning is employed to allow for different numbers of low and high-fidelity training points, by using an additional GCN-based low-fidelity map to predict high fidelity outputs. It is shown on 4 different data sets that a significant (at least one order of magnitude) increase in accuracy is obtained, using one to two orders of magnitude fewer low and high fidelity training points. One of the data sets is developed in this work, pertaining to 1000 simulations of quinone molecules (up to 24 atoms) at 5 different levels of fidelity, furnishing the energy, dipole moment and HOMO/LUMO.

Keywords: .materials screening, computational materials, machine learning, multi-fidelity, graph convolutional network, semi-supervised learning

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Authors: Tigabu Dagne Akal

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While advances in computer and communications technology have made the network ubiquitous, they have also rendered networked systems vulnerable to malicious attacks devised from a distance. These attacks or intrusions start with attackers infiltrating a network through a vulnerable host and then launching further attacks on the local network or Intranet. Nowadays, system administrators and network professionals can attempt to prevent such attacks by developing intrusion detection tools and systems using data mining technology. In this study, the experiments were conducted following the Knowledge Discovery in Database Process Model. The Knowledge Discovery in Database Process Model starts from selection of the datasets. The dataset used in this study has been taken from Massachusetts Institute of Technology Lincoln Laboratory. After taking the data, it has been pre-processed. The major pre-processing activities include fill in missed values, remove outliers; resolve inconsistencies, integration of data that contains both labelled and unlabelled datasets, dimensionality reduction, size reduction and data transformation activity like discretization tasks were done for this study. A total of 21,533 intrusion records are used for training the models. For validating the performance of the selected model a separate 3,397 records are used as a testing set. For building a predictive model for intrusion detection J48 decision tree and the Naïve Bayes algorithms have been tested as a classification approach for both with and without feature selection approaches. The model that was created using 10-fold cross validation using the J48 decision tree algorithm with the default parameter values showed the best classification accuracy. The model has a prediction accuracy of 96.11% on the training datasets and 93.2% on the test dataset to classify the new instances as normal, DOS, U2R, R2L and probe classes. The findings of this study have shown that the data mining methods generates interesting rules that are crucial for intrusion detection and prevention in the networking industry. Future research directions are forwarded to come up an applicable system in the area of the study.

Keywords: intrusion detection, data mining, computer science, data mining

Procedia PDF Downloads 285

Authors: E. Oliver, A. Cebolla, A. Dominguez, A. Gonzalez-Segura, E. de la Cruz, S. Albertini, L. Ferrini, K. Kronika, T. Nilsen, R. Baños

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The main objective of MEAL is to develop a pedagogical tool aimed to help teachers and nutritionists (students and professionals) to acquire, train, promote and deliver to children basic nutritional education and healthy eating behaviours competencies. MEAL is focused on eating behaviours and not only in nutritional literacy, and will use new technologies like Information and Communication Technologies (ICTs) and serious games (SG) platforms to consolidate the nutritional competences and habits.

Keywords: nutritional education, pedagogical ICT platform, serious games, training course

Procedia PDF Downloads 511

Authors: Helge Rupprich, Elke Knisel

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In a time of mediazation and reduced physical movement, it is important to change passivity (akinesa) into physical activity to improve health. The approach is to encourage children, junior athletes, recreational athletes, and semi-professional athletes to do sports while attending a top sport event. The concept has the slogan: get out off your seat and move! A top sport event of a series of professional beach volleyball tournaments with 330.000 life viewers, 13,70 million cumulative reach viewers and 215,13 million advertising contacts is used as framework for different sports didactic approaches, social integrative approaches and migration valuations. An important aim is to use the big radiant power of the top sport event to extract active participants from the viewers of the top sport event. Even if it is the goal to improve physical activity, it is necessary to differentiate between the didactic approaches. The first approach contains psycho motoric exercises with children (N=158) between two and five years which was used in the project ‘largest sandbox of the city’. The second approach is social integration and promotion of activity of students (N=54) in the form of a student beach volleyball tournament. The third approach is activity in companies. It is based on the idea of health motivation of employees (N=62) in a big beach volleyball tournament. Fourth approach is to improve the sports leisure time activities of recreational athletes (N=292) in different beach volleyball tournaments. Fifthly approach is to build a foreign friendly measure which is implemented in junior athlete training with the French and German junior national team (N=16). Sixthly approach is to give semi professional athletes a tournament to develop their relation to active life. Seventh approach is social integration for disadvantaged people (N=123) in form of training with professional athletes. The top sport beach volleyball tournament had 80 athletes (N=80) and 34.000 viewers. In sum 785 athletes (N=785) did sports in 13 days. Over 34.000 viewers where counted in the first three days of top sport event. The project was evaluated positively by the City of Dresden, Politics of Saxony and the participants and will be continued in Dresden and expanded for the season 2015 in Jena.

Keywords: beach volleyball, event, sports didactic, sports project

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Authors: Theodrose Sisay, Kindie Tesfaye, Mengistu Ketema, Nigussie Dechassa, Mezegebu Getnet

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Agriculture is a sector that is very vulnerable to the effects of climate change and contributes to anthropogenic greenhouse gas (GHG) emissions in the atmosphere. By lowering emissions and adjusting to the change, it can also help to reduce climate change. Utilizing Climate-Smart Agriculture (CSA) technology that can sustainably boost productivity, improve resilience, and lower GHG emissions is crucial. This study sought to identify the CSA technologies used by farmers and assess adoption levels and factors that influence them. In order to gather information from 384 smallholder farmers in the Great Rift Valley (GRV) of Ethiopia, a cross-sectional survey was carried out. Data were analysed using percentage, chi-square test, t-test, and multivariate probit model. Results showed that crop diversification, agroforestry, and integrated soil fertility management were the most widely practiced technologies. The results of the Chi-square and t-tests showed that there are differences and significant and positive connections between adopters and non-adopters based on various attributes. The chi-square and t-test results confirmed that households who were older had higher incomes, greater credit access, knowledge of the climate, better training, better education, larger farms, higher incomes, and more frequent interactions with extension specialists had a positive and significant association with CSA technology adopters. The model result showed that age, sex, and education of the head, farmland size, livestock ownership, income, access to credit, climate information, training, and extension contact influenced the selection of CSA technologies. Therefore, effective action must be taken to remove barriers to the adoption of CSA technologies, and taking these adoption factors into account in policy and practice is anticipated to support smallholder farmers in adapting to climate change while lowering emissions.

Keywords: climate change, climate-smart agriculture, smallholder farmers, multivariate probit model

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Authors: Mohsina Aktar, Bishawjit Mallick

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Bangladesh faces the multidimensional manifestations of climate change e.g. flood, cyclone, sea level rise, drainage congestion, salinity, etc. This study aimed at to find out the community’s perception of the perceived impact of climate change on their wetland resource based livelihood, to analyze their present livelihood scenario and to find out required institutional setup to strengthen present livelihood scenario. Therefore, this study required both quantitative analysis like quantification of wetland resources, occupation, etc. and also exploratory information like policy and institutional reform. For quantitative information 200 questionnaire survey and in some cases observation survey and for socially shareable qualitative and quantitative issues case study and focus group discussion were conducted. In-Depth interview was conducted for socially non-shareable qualitative issues. The overall findings of this study have been presented maintaining a sequence- perception about climate change effect, livelihood scenario and required institutional support of the wetland community. Flood has been ranked where cyclone effect is comparatively less disastrous in this area. Heavy rainfall comes after the cyclone. Female members responded almost same about the ranking and effects of frequently occurred and devastating effects of climate change. People are much more aware of the impact of climate change. Training of Care in RVCC project helps to increase their knowledge level. If the level of education can be increased, people can fight against calamity and poverty with more confidence. People seem to overcome the problems of water logging and thus besides involving in Hydroponics (33.3%) as prime occupation in monsoon; they are also engaged in other business related activities. January to May is the low-income season for the farmers. But some people don’t want to change their traditional occupation and their age is above 45. The young earning member wants to utilize their lean income period by alternative occupation. People who do not have own land and performing water transportation or other types of occupation are now interested about Hydroponics. People who give their land on rent are now thinking about renting their land in monsoon as through that they can earn a sound amount rather than get nothing. What they require is just seed, training, and capital. Present marketing system faces the problem of communication. So this sector needed to be developed. Involvement of women in income earning activity is very low (5.1%), and 100% women are housewives. They became inferior due to their educational level and dominance of their husband. Only one NGO named BCAS (Bangladesh Center for Advanced Studies) has been found engage training facilities and advocacy for this purpose. Upazilla agricultural extension office like other GO remains inactive to give support the community for extension and improvement of Hydroponics agriculture. If the community gets proper support and inspiration, they can fight against crisis of low-income and climate change, with the Hydroponics cultivation system successfully.

Keywords: wetland community, hydroponics, climate change adaptation, livelihood

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Authors: Trevor Stephen, Joshua D. Aceves, David Guyer, Jona Jacobson

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As a profession, social work has much to offer individuals, groups, and organizations. A multidisciplinary approach to understanding and solving complex challenges and a commitment to developing and training ethical practitioners outlines characteristics of a profession embedded with leadership skills. This presentation will take an overview of the historical context of social work leadership, examine social work as a unique leadership model composed of its qualities and theories that inform effective leadership capability as it relates to our code of ethics. Reflect critically on leadership theories and their foundational comparison. Finally, a look at recommendations and implementation to social work education and practice. Similar to defining leadership, there is no universally accepted definition of social work leadership. However, some distinct traits and characteristics are essential. Recent studies help set the stage for this research proposal because they measure views on effective social work leadership among social work and non-social leaders and followers. However, this research is interested in working backward from that approach and examining social workers' leadership preparedness perspectives based solely on social work training, competencies, values, and ethics. Social workers understand how to change complex structures and challenge resistance to change to improve the well-being of organizations and those they serve. Furthermore, previous studies align with the idea of practitioners assessing their skill and capacity to engage in leadership but not to lead. In addition, this research is significant because it explores aspiring social work leaders' competence to translate social work practice into direct leadership skills. The research question seeks to answer whether social work training and competencies are sufficient to determine whether social workers believe they possess the capacity and skill to engage in leadership practice. Aim 1: Assess whether social workers have the capacity and skills to assume leadership roles. Aim 2: Evaluate how the development of social workers is sufficient in defining leadership. This research intends to reframe the misconception that social workers do not possess the capacity and skills to be effective leaders. On the contrary, social work encompasses a framework dedicated to lifelong development and growth. Social workers must be skilled, competent, ethical, supportive, and empathic. These are all qualities and traits of effective leadership, whereas leaders are in relation with others and embody partnership and collaboration with followers and stakeholders. The proposed study is a cross-sectional quasi-experimental survey design that will include the distribution of a multi-level social work leadership model and assessment tool. The assessment tool aims to help define leadership in social work using a Likert scale model. A cross-sectional research design is appropriate for answering the research questions because the measurement survey will help gather data using a structured tool. Other than the proposed social work leadership measurement tool, there is no other mechanism based on social work theory and designed to measure the capacity and skill of social work leadership.

Keywords: leadership competencies, leadership education, multi-level social work leadership model, social work core values, social work leadership, social work leadership education, social work leadership measurement tool

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Authors: Giorgi Methodishvili, Iuliia Methodishvili

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In this paper product development algorithm is used to determine the optimal management of financial resources in company. Aspects of financial management considered include put initial investment, examine all possible ways to solve the problem and the optimal rotation length of profit. The software of given problems is based using greedy algorithm. The obtained model and program maintenance enable us to define the optimal version of management of proper financial flows by using visual diagram on each level of investment.

Keywords: management, software, optimal, greedy algorithm, graph-diagram

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Authors: Nandu C. Nair, Kamal Bijlani

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This paper proposes a concept-based assessment to track the performance of the students. The idea behind this approach is to map the exam questions with the concepts learned in the course. So at the end of the course, each student will know how well he learned each concept. This system will give a self assessment for the students as well as instructor. By analyzing the score of all students, instructor can decide some concepts need to be teaching again or not. The system’s efficiency is proved using three courses from M-tech program in E-Learning technologies and results show that the concept-wise assessment improved the score in final exam of majority students on various courses.

Keywords: assessment, concept, examination, question, score

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Authors: Kan Yu, Khui Hung Lee, Eben Afrifa-Yamoah, Jing Guo, Katrina Harrison, Jack Goldblatt, Nicholas Pachter, Jitian Xiao, Guicheng Brad Zhang

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Background and Significance of the Study: Congenital Heart Defects (CHDs) are the most common malformation at birth and one of the leading causes of infant death. Although the exact etiology remains a significant challenge, epigenetic modifications, such as DNA methylation, are thought to contribute to the pathogenesis of congenital heart defects. At present, no existing DNA methylation biomarkers are used for early detection of CHDs. The existing CHD diagnostic techniques are time-consuming and costly and can only be used to diagnose CHDs after an infant was born. The present study employed a machine learning technique to analyse genome-wide methylation data in children with and without CHDs with the aim to find methylation biomarkers for CHDs. Methods: The Illumina Human Methylation EPIC BeadChip was used to screen the genome‐wide DNA methylation profiles of 24 infants diagnosed with congenital heart defects and 24 healthy infants without congenital heart defects. Primary pre-processing was conducted by using RnBeads and limma packages. The methylation levels of top 600 genes with the lowest p-value were selected and further investigated by using a random forest approach. ROC curves were used to analyse the sensitivity and specificity of each biomarker in both training and test sample sets. The functionalities of selected genes with high sensitivity and specificity were then assessed in molecular processes. Major Findings of the Study: Three genes (MIR663, FGF3, and FAM64A) were identified from both training and validating data by random forests with an average sensitivity and specificity of 85% and 95%. GO analyses for the top 600 genes showed that these putative differentially methylated genes were primarily associated with regulation of lipid metabolic process, protein-containing complex localization, and Notch signalling pathway. The present findings highlight that aberrant DNA methylation may play a significant role in the pathogenesis of congenital heart defects.

Keywords: biomarker, congenital heart defects, DNA methylation, random forest

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Authors: Suraj Mehrotra

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The stock market is a volatile, bustling marketplace that is a cornerstone of economics. It defines whether companies are successful or in spiral. A thorough understanding of it is important - many companies have whole divisions dedicated to analysis of both their stock and of rivaling companies. Linking the world of finance and artificial intelligence (AI), especially the stock market, has been a relatively recent development. Predicting how stocks will do considering all external factors and previous data has always been a human task. With the help of AI, however, machine learning models can help us make more complete predictions in financial trends. Taking a look at the stock market specifically, predicting the open, closing, high, and low prices for the next day is very hard to do. Machine learning makes this task a lot easier. A model that builds upon itself that takes in external factors as weights can predict trends far into the future. When used effectively, new doors can be opened up in the business and finance world, and companies can make better and more complete decisions. This paper explores the various techniques used in the prediction of stock prices, from traditional statistical methods to deep learning and neural networks based approaches, among other methods. It provides a detailed analysis of the techniques and also explores the challenges in predictive analysis. For the accuracy of the testing set, taking a look at four different models - linear regression, neural network, decision tree, and naïve Bayes - on the different stocks, Apple, Google, Tesla, Amazon, United Healthcare, Exxon Mobil, J.P. Morgan & Chase, and Johnson & Johnson, the naïve Bayes model and linear regression models worked best. For the testing set, the naïve Bayes model had the highest accuracy along with the linear regression model, followed by the neural network model and then the decision tree model. The training set had similar results except for the fact that the decision tree model was perfect with complete accuracy in its predictions, which makes sense. This means that the decision tree model likely overfitted the training set when used for the testing set.

Keywords: machine learning, testing set, artificial intelligence, stock analysis

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Authors: Maryam Tabrizi, Shahrzad Aarup

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Objectives- Understanding the close association between oral health and overall health for older adults at the right time and right place, a person, focus treatment through Project ECHO telementoring. Methodology- Data from monthly ECHO telementoring sessions were provided for three years. Sessions including case presentations, overall health conditions, considering medications, organ functions limitations, including the level of cognition. Contributions- Providing the specialist level of providing care to all elderly regardless of their location and other health conditions and decreasing oral health inequity by increasing workforce via Project ECHO telementoring program worldwide. By 2030, the number of adults in the USA over the age of 65 will increase more than 60% (approx.46 million) and over 22 million (30%) of 74 million older Americans will need specialized geriatrician care. In 2025, a national shortage of medical geriatricians will be close to 27,000. Most individuals 65 and older do not receive oral health care due to lack of access, availability, or affordability. One of the main reasons is a significant shortage of Oral Health (OH) education and resources for the elderly, particularly in rural areas. Poor OH is a social stigma, a thread to quality and safety of overall health of the elderly with physical and cognitive decline. Poor OH conditions may be costly and sometimes life-threatening. Non-traumatic dental-related emergency department use in Texas alone was over $250 M in 2016. Most elderly over the age of 65 present with at least one or multiple chronic diseases such as arthritis, diabetes, heart diseases, and chronic obstructive pulmonary disease (COPD) are at higher risk to develop gum (periodontal) disease, yet they are less likely to get dental care. In addition, most older adults take both prescription and over-the-counter drugs; according to scientific studies, many of these medications cause dry mouth. Reduced saliva flow due to aging and medications may increase the risk of cavities and other oral conditions. Most dental schools have already increased geriatrics OH in their educational curriculums, but the aging population growth worldwide is faster than growing geriatrics dentists. However, without the use of advanced technology and creating a network between specialists and primary care providers, it is impossible to increase the workforce, provide equitable oral health to the elderly. Project ECHO is a guided practice model that revolutionizes health education and increases the workforce to provide best-practice specialty care and reduce health disparities. Training oral health providers for utilizing the Project ECHO model is a logical response to the shortage and increases oral health access to the elderly. Project ECHO trains general dentists & hygienists to provide specialty care services. This means more elderly can get the care they need, in the right place, at the right time, with better treatment outcomes and reduces costs.

Keywords: geriatric, oral health, project echo, chronic disease, oral health

Procedia PDF Downloads 159

Authors: Mane Varosyan, Diana Tumanyan, Agnesa Martirosyan

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There are many catastrophic events that end with dire consequences, and to avoid them, people should be well-armed with the necessary information about these situations. During the last years, Serious Games have increasingly gained popularity for training people for different types of emergencies. The major discussed problem is the usage of gamification in education. Moreover, it is mandatory to understand how and what kind of gamified e-learning modules promote engagement. As the theme is emergency, we also find out people’s behavior for creating the final approach. Our proposed solution is an educational video game, “EZZRA”.

Keywords: gamification, education, emergency, serious games, game design, virtual reality, digitalisation

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Authors: Yanting Cao, Kazumitsu Nawata

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As smartphones upgrading, their software and hardware are getting smarter, so the smartphone-based human activity recognition will be described as more refined, complex, and detailed. In this context, we analyzed a set of experimental data obtained by observing and measuring 30 volunteers with six activities of daily living (ADL). Due to the large sample size, especially a 561-feature vector with time and frequency domain variables, cleaning these intractable features and training a proper model becomes extremely challenging. After a series of feature selection and parameters adjustment, a well-performed SVM classifier has been trained.

Keywords: smart sensors, human activity recognition, artificial intelligence, SVM

Procedia PDF Downloads 132

Authors: Liang Cheng Chang, Wei Ju Huang, You Cheng Chen

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Investigation and research are keys for conjunctive use of surface and groundwater resources. The hydrogeological structure is an important base for groundwater analysis and simulation. Traditionally, the hydrogeological structure is artificially determined based on geological drill logs, the structure of wells, groundwater levels, and so on. In Taiwan, groundwater observation network has been built and a large amount of groundwater-level observation data are available. The groundwater level is the state variable of the groundwater system, which reflects the system response combining hydrogeological structure, groundwater injection, and extraction. This study applies analytical tools to the observation database to develop a methodology for the identification of confined and unconfined aquifers. These tools include frequency analysis, cross-correlation analysis between rainfall and groundwater level, groundwater regression curve analysis, and decision tree. The developed methodology is then applied to groundwater layer identification of two groundwater systems: Zhuoshui River alluvial fan and Pingtung Plain. The abovementioned frequency analysis uses Fourier Transform processing time-series groundwater level observation data and analyzing daily frequency amplitude of groundwater level caused by artificial groundwater extraction. The cross-correlation analysis between rainfall and groundwater level is used to obtain the groundwater replenishment time between infiltration and the peak groundwater level during wet seasons. The groundwater regression curve, the average rate of groundwater regression, is used to analyze the internal flux in the groundwater system and the flux caused by artificial behaviors. The decision tree uses the information obtained from the above mentioned analytical tools and optimizes the best estimation of the hydrogeological structure. The developed method reaches training accuracy of 92.31% and verification accuracy 93.75% on Zhuoshui River alluvial fan and training accuracy 95.55%, and verification accuracy 100% on Pingtung Plain. This extraordinary accuracy indicates that the developed methodology is a great tool for identifying hydrogeological structures.

Keywords: aquifer identification, decision tree, groundwater, Fourier transform

Procedia PDF Downloads 145

Authors: Hsin-Chieh Lee, Fen-Ling Kuo, Jui-Chi Lin

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Background: Upper extremity functional impairment that occurs after stroke includes hemiplegia, synergy movement, muscle hypertonicity, and somatosensory impairment, which result in inefficient and inaccurate movement. Robot-assisted rehabilitation is an intensive training approach that is effective in sensorimotor and hand function recovery. However, these systems mostly focused on the proximal part of the upper limb rather than the distal part. The device used in our study was Gloreha Sinfonia, which focuses on the distal part of the upper limb and uses a dynamic support system to facilitate the whole limb function. The objective of this study was to investigate the effects of robot-assisted therapy (RT) with Gloreha device on sensorimotor, and ADLs in patients with stroke. Method: Patients with stroke (N=25) participated AB or BA (A = 12 RT sessions and B = 12 conventional therapy (CT) sessions) for 6 weeks (60 min at each session, twice a week), with 1-month break for washout period. The performance of the patients was assessed by a blinded assessor at 4 time points (pretest 1, posttest 1, pretest 2, posttest 2) which including the Fugl–Meyer Assessment-upper extremity (FMA-UE), box and block test, electromyography of the extensor digitorum communis (EDC) and brachioradialis, a grip dynamometer for motor evaluation; Semmes–Weinstein hand monofilament and Revision of the Nottingham Sensory Assessment for sensory evaluation; and the Modified Barthel Index (MBI) for assessing the ADL ability. Result: RT group significantly improved FMA-UE proximal scores (p = 0.038), FMA-UE total scores (p = 0.046), and MBI (p = 0.030). The EDC exhibited higher efficiency during the small block grasping task in the RT group than in the CT group (p = 0.050). Conclusions: RT with the Gloreha device might lead to beneficial effects on arm motor function, ADL ability, and EDC muscle recruitment efficacy in patients with subacute to chronic stroke.

Keywords: activities of daily living, hand function, robotic rehabilitation, stroke

Procedia PDF Downloads 103

Authors: Anupam Sibal

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Apollo Quality Program(AQP) was launched across the Apollo Group of Hospitals to address the four patient safety areas; Safety during Clinical Handovers, Medication Safety, Surgical Safety and the six International Patient Safety Goals(IPSGs) of JCI. A measurable, online, quality dashboard covering 20 process and outcome parameters was devised for monthly monitoring. The expected outcomes were also defined and categorized into green, yellow and red ranges. An audit methodology was also devised to check the processes for the measurable dashboard. Documented clinical handovers were introduced for the first time at many locations for in-house patient transfer, nursing-handover, and physician-handover. Prototype forms using the SBAR format were made. Patient-identifiers, read-back for verbal orders, safety of high-alert medications, site marking and time-outs and falls risk-assessment were introduced for all hospitals irrespective of accreditation status. Measurement of Surgical-Site-Infection (SSI) for 30 days postoperatively, was done. All hospitals now tracked the time of administration of antimicrobial prophylaxis before surgery. Situations with high risk of retention of foreign body were delineated and precautionary measures instituted. Audit of medications prescribed in the discharge summaries was made uniform. Formularies, prescription-audits and other means for reduction of medication errors were implemented. There is a marked increase in the compliance to processes and patient safety outcomes. Compliance to read-back for verbal orders rose from 86.83% in April’11 to 96.95% in June’15, to policy for high alert medications from 87.83% to 98.82%, to use of measures to prevent wrong-site, wrong-patient, wrong procedure surgery from 85.75% to 97.66%, to hand-washing from 69.18% to 92.54%, to antimicrobial prophylaxis within one hour before incision from 79.43% to 93.46%. Percentage of patients excluded from SSI calculation due to lack of follow-up for the requisite time frame decreased from 21.25% to 10.25%. The average AQP scores for all Apollo Hospitals improved from 62 in April’11 to 87.7 in Jun’15.

Keywords: clinical handovers, international patient safety goals, medication safety, surgical safety

Procedia PDF Downloads 246

Authors: Bonnie R. Brammall, Rhonda M. Garad, Helena J. Teede, Cheryce L. Harrison

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Introduction: Reproductive aged women are at high-risk for accelerated weight gain and obesity development, with pregnancy recognised as a critical contributory life phase. Healthy lifestyle interventions during the preconception and antenatal period improve maternal and infant health outcomes. Yet, interventions from preconception through to postpartum and translation and implementation into real-world healthcare settings remain limited. OptimalMe is a randomised, hybrid implementation effectiveness study of evidence-based healthy lifestyle intervention. Here, we report engagement, acceptability of the intervention during preconception, and self-reported behaviour change outcomes as a result of the preconception phase of the intervention. Methods: Reproductive aged women who upgraded their private health insurance to include pregnancy and birth cover, signalling a pregnancy intention, were invited to participate. Women received access to an online portal with preconception health and lifestyle modules, goal-setting and behaviour change tools, monthly SMS messages, and two coaching sessions (randomised to video or phone) prior to pregnancy. Results: Overall n=527 expressed interest in participating. Of these, n=33 did not meet inclusion criteria, n=8 were not contactable for eligibility screening, and n=177 failed to engage after the screening, leaving n=309 who were enrolled in OptimalMe and randomised to intervention delivery method. Engagement with coaching sessions dropped by 25% for session two, with no difference between intervention groups. Women had a mean (SD) age of 31.7 (4.3) years and, at baseline, a self-reported mean BMI of 25.7 (6.1) kg/m², with 55.8% (n=172) of a healthy BMI. Behaviour was sub-optimal with infrequent self-weighing (38.1%), alcohol consumption prevalent (57.1%), sub-optimal pre-pregnancy supplementation (61.5%), and incomplete medical screening. Post-intervention 73.2% of women reported engagement with a GP for preconception care and improved lifestyle behaviour (85.5%), since starting OptimalMe. Direct pre-and-post comparison of individual participant data showed that of 322 points of potential change (up-to-date cervical screening, elimination of high-risk behaviours [alcohol, drugs, smoking], uptake of preconception supplements and improved weighing habits) 158 (49.1%) points of change were achieved. Health coaching sessions were found to improve accountability and confidence, yet further personalisation and support were desired. Engagement with video and phone sessions was comparable, having similar impacts on behaviour change, and both methods were well accepted and increased women's accountability. Conclusion: A low-intensity digital health and lifestyle program with embedded health coaching can improve the uptake of preconception care and lead to self-reported behaviour change. This is the first program of its kind to reach an otherwise healthy population of women planning a pregnancy. Women who were otherwise healthy showed divergence from preconception health and lifestyle objectives and benefited from the intervention. OptimalMe shows promising results for population-based behaviour change interventions that can improve preconception lifestyle habits and increase engagement with clinical health care for pregnancy preparation.

Keywords: preconception, pregnancy, preventative health, weight gain prevention, self-management, behaviour change, digital health, telehealth, intervention, women's health

Procedia PDF Downloads 81

Authors: Madrine King'endo

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This study dealt with the influence of spelling errors on English language performance among learners with dysgraphia in public primary schools in West Embu, Embu County, Kenya. The study purposed to investigate the influence of spelling errors on the English language performance among the class three pupils with dysgraphia in public primary schools. The objectives of the study were to identify the spelling errors that learners with dysgraphia make when writing English words and classify the spelling errors they make. Further, the study will establish how the spelling errors affect the performance of the language among the study participants, and suggest the remediation strategies that teachers could use to address the errors. The study could provide the stakeholders with relevant information in writing skills that could help in developing a responsive curriculum to accommodate the teaching and learning needs of learners with dysgraphia, and probably ensure training of teachers in teacher training colleges is tailored within the writing needs of the pupils with dysgraphia. The study was carried out in Embu county because the researcher did not find any study in related literature review concerning the influence of spelling errors on English language performance among learners with dysgraphia in public primary schools done in the area. Moreover, besides being relatively populated enough for a sample population of the study, the area was fairly cosmopolitan to allow a generalization of the study findings. The study assumed the sampled schools will had class three pupils with dysgraphia who exhibited written spelling errors. The study was guided by two spelling approaches: the connectionist stimulation of spelling process and orthographic autonomy hypothesis with a view to explain how participants with learning disabilities spell written words. Data were collected through interviews, pupils’ exercise books, and progress records, and a spelling test made by the researcher based on the spelling scope set for class three pupils by the ministry of education in the primary education syllabus. The study relied on random sampling techniques in identifying general and specific participants. Since the study used children in schools as participants, voluntary consent was sought from themselves, their teachers and the school head teachers who were their caretakers in a school setting.

Keywords: dysgraphia, writing, language, performance

Procedia PDF Downloads 141

Authors: Bernard Morais Joosa

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Kerala, the southern state of India, is known for its accomplishments in universal education and enrollments. Through this mission, the Government proposes comprehensive educational reforms including 1000 Government schools into international standards during the first phase. The idea is not only to improve the infrastructural facilities but also to reform the teaching and learning process to the present day needs by introducing ICT enabled learning and providing smart classrooms. There will be focus on creating educational programmes which are useful for differently abled students. It is also meant to reinforce the teaching–learning process by providing ample opportunities to each student to construct their own knowledge using modern technology tools. The mission will redefine the existing classroom learning process, coordinate resource mobilization efforts and develop ‘Janakeeya Vidyabhyasa Mathruka.' Special packages to support schools which are in existence for over 100 years will also be attempted. The implementation will enlist full involvement and partnership of the Parent Teacher Association. Kerala was the first state in the country to attain 100 percent literacy more than two and a half decades ago. Since then the State has not rested on its laurels. It has moved forward in leaps and bounds conquering targets that no other State could achieve. Now the government of Kerala is taking off towards new goal of comprehensive educational reforms. And it focuses on Betterment of educational surroundings, use of technology in education, renewal of learning method and 1000 schools will be uplifted as Smart Schools. Need to upgrade 1000 schools into international standards and turning classrooms from standard 9 to 12 in high schools and higher secondary into high-tech classrooms and a special unique package for the renovation of schools, which have completed 50 and 100 years. The government intends to focus on developing standards first to eighth standards in tune with the times by engaging the teachers, parents, and alumni to recapture the relevance of public schools. English learning will be encouraged in schools. The idea is not only to improve the infrastructure facilities but also reform the curriculum to the present day needs. Keeping in view the differently-abled friendly approach of the government, there will be focus on creating educational program which is useful for differently abled students. The idea is to address the infrastructural deficiencies being faced by such schools. There will be special emphasis on ensuring internet connectivity to promote IT-friendly existence. A task-force and a full-time chief executive will be in charge of managing the day to day affairs of the mission. Secretary of the Public Education Department will serve as the Mission Secretary and the Chairperson of Task Force. As the Task Force will stress on teacher training and the use of information technology, experts in the field, as well as Directors of SCERT, IT School, SSA, and RMSA, will also be a part of it.

Keywords: educational standards, methodology, pedagogy, technology

Procedia PDF Downloads 119

Authors: N. Khanyile, P. Dlamini, M. Masenya

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Background: Most of the population in Africa is still dependent on indigenous medicinal knowledge for treating and managing ailments. Indigenous traditional knowledge owners/practitioners who own this knowledge are consulted by communities, but their knowledge is not known how they get it. The question of how knowledge is acquired and preserved remains one of the biggest challenges in traditional healing and treatment with herbal medicines. It is regrettable that despite the importance and recognition of indigenous medicinal knowledge globally, the details of acquirement, storing and transmission, and preservation techniques are not known. Hence this study intends to unveil the process of acquirement and transmission, and preservation techniques of indigenous medical knowledge by its owners. Objectives: This study aims to assess the process of acquiring and preservation of traditional medicinal knowledge by traditional medicinal knowledge owners/practitioners in uMhlathuze Municipality, in the province of KwaZulu-Natal, South Africa. The study was guided by four research objectives which were to: identify the types of traditional medicinal knowledge owners who possess this knowledge, establish the approach used by indigenous medicinal knowledge owners/healers for acquiring medicinal knowledge, identify the process of preservation of medicinal knowledge by indigenous medicinal knowledge owners/healers, and determine the challenges encountered in transferring the knowledge. Method: The study adopted a qualitative research approach, and a snowball sampling technique was used to identify the study population. Data was collected through semi-structured interviews with indigenous medicinal knowledge owners. Results: The findings suggested that uMhlathuze municipality had different types of indigenous medicinal knowledge owners who possess valuable knowledge. These are diviners (Izangoma), faith healers (Abathandazi), and herbalists (Izinyanga). The study demonstrated that indigenous medicinal knowledge is acquired in many different ways, including visions, dreams, and vigorous training. The study also revealed the acquired knowledge is preserved or shared with specially chosen children and trainees. Conclusion: The study concluded that this knowledge is gotten through vigorous training, which requires the learner to be attentive and eager to learn. It was recommended that a study of this nature be conducted but at a broader level to enhance an informed conclusion and recommendations.

Keywords: preserving, indigenous medicinal knowledge, indigenous knowledge, indigenous medicinal knowledge owners/practitioners, acquiring

Procedia PDF Downloads 74

Authors: Ali Isa Danlami

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This study examined the coaching leadership behaviours preferred by athletes in individual and team sports in Nigeria that may lead to increased satisfaction and performance. Six leadership behaviours were identified; these are democratic, training and instruction, situational consideration, autocratic, social support and positive feedback. The six leadership behaviours relate to the preference of coaches by athletes that leads to increased performance were the focus of this study. The population of this study is comprised of male and female athletes of states sports councils in Nigeria. An ex-post facto research design was employed for this study. Stratified and purposive sampling techniques were used to select the sampled states according to the six geo-political zones of the country. Two states (North Central (FCT, Nasarawa), North East (Bauchi, Gombe), North West (Kaduna, Sokoto), South East (Anambra, Imo), South west (Ogun, Ondo), South South (Delta, and Rivers) were selected from each stratum. A modified questionnaire was used to collect data for this study, and the data collected were subjected to a reliability test using the Statistical Package for Social Science (SPSS) to analyse the data. A two sample Z-test procedure was used to test the significant differences because of the large number of subjects involved in the different groups. All hypotheses were tested at 0.05 alpha value. The findings of the study concluded that: Athletes in team and individual sports generally preferred coaches who were more disposed towards training and instructions, social support, positive feedback, situational consideration and democratic behaviours. It was also found that athletes in team sports have higher preference for coaches with democratic behaviour. The result revealed that athletes in team and individual sports did not have a preference for coaches disposed towards autocratic behaviour. Based on this, the following recommendations were made: Democratic behaviour by coaches should be encouraged in team and individual sports. Coaches should not be engaged in autocratic behaviours when coaching. These behaviours should be adopted by coaches to increase athletes’ satisfaction and enhancement in performance.

Keywords: leadership behaviour, preference, athletes, individual, team, coaches’

Procedia PDF Downloads 118