Search results for: individual health

Authors: Marianne Paimre

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The COVID-19 crisis has prompted older people to adopt new technologies to facilitate their daily life. This study explored the relationships between socioeconomic indicators, technology acceptance, online health information seeking (OHIS), and health behavior (HB), including readiness for COVID-19 vaccination among Estonian older adults. A cross-sectional survey was conducted among 501 people aged ≥ 50 in 2020. Its findings indicate that the more recurrent the need for health information was (rho = .11, p<.05), and the more regularly one searched for it (rho = .14, p<.01), the more willing a person was to get vaccinated. Also, interest in digital applications corresponded to vaccination readiness (rho = .25, p<.001). However, this relationship did not emerge in the case of other health behaviors such as healthy diet and exercise. Differences in health information behavior (HIB) should be considered when developing effective means of health communication designed especially for crisis situations.

Keywords: older adults, technology acceptance, health information behavior, health behavior, COVID-19 pandemic

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Authors: Rosana Silvina Codaro, Patricia Amelia Tomei

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This study explores the relationship between job satisfaction and alignment between the individual´s current occupation and his talents, needs and values, namely his 'career anchors'. With this purpose in mind, a quantitative survey was performed for a non- graduate probabilistic sample of management business students of a private university in Rio de Janeiro. The results of the survey showed there is no significant association between satisfaction at work and alignment with the individual’s career anchor. The most frequent career anchor found for both genders was lifestyle, showing a trend towards finding a career that allows some balance between professional and personal life. The study also showed that self-employed individuals are more satisfied with their work than the individuals employed by a company are, and men are more satisfied at work than women are, Individuals aligned and not satisfied tend to be the ones who have fewer years of work experience and individuals not aligned and satisfied tend to be older.

Keywords: careers, career anchors, job satisfaction, Schein´s career anchor model

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Authors: Shiksha Thakur, Rashmi Jain

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Maternal health is the most crucial element in the primary health care delivery system of any healthy society. We aware that the maternal health situation in India has been a cause of concern for us, in spite of the rapidly progressing socio-economic environment overall. India has realized impressive gains in Mother & Child survival over the last two decades. MMR as per 2012-2013 in India is 167 as per MMR bulletin, though there are variations between states in the Country. In 2013, an estimated 2,89,000 women worldwide died from complications arising from pregnancy & childbirth. In view of the above facts, a study was conducted in Indore to analyse the maternal health status among urban slums and non-slums women.

Keywords: antenatal care, postnatal care, JSY, maternal health, child health, reproductive health

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Authors: Anushree S. Panikkassery

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The paper analyses the pattern and trend of maternal health care in south Indian states. It studies the interstate disparities in terms of maternal health care. It also compares the trends in terms of achieving the target of sustainable development Goal is related to maternal health. The maternal health care (MHC) development is one of the key indicators for the development of health sector in the country and assumes significance from the socioeconomic and developmental perspectives. Maternal health care mainly consists of composite care during pregnancy, child birth as well as postpartum period. Antenatal care, identification, referral and management of high risk pregnancies, safe and healthy child birth and early postnatal care are some of the important issues pertaining to maternal health. Data is collected from national family health survey 1992-93, 1998-99, 2005-06, and 2015-16. A concentration index is used to study the disparities in equity of maternal health among south Indian states. The study shows that there has been an improvement in maternal health care in south Indian states with Kerala topping among the states. But there exist disparities among the south Indian states.

Keywords: antenatal care, disparities, equity, maternal health

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Authors: Zeiler M., Stadler D., Schmaderer C.

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Over the years of dialysis treatment, most patients experience significant weight loss. The primary emphasis in earlier research was the underlying mechanism of protein energy wasting and the subsequent malnutrition inflammation syndrome. In the interest to provide an effective and rapid solution for the patients, the aim of this study is identifying individual influences of their assumed reduced dietary intake, such as nausea, appetite loss and taste changes, and to determine whether the patients adhere to their nutrition guidelines. A prospective, controlled study with 38 end-stage renal disease patients was performed using a questionnaire to reflect their diet within the last 12 months. Thereby, the daily intake for the most important macro-and micronutrients was calculated to be compared with the individual KDQOI-guideline value, as well as controls matched in age and gender. The majority of the study population did not report symptoms commonly associated with dialysis, such as nausea or inappetence, and denied any change in dietary behavior since receiving renal replacement therapy. The patients’ daily intake of energy (3080kcal ± 1266) and protein (89,9g [53,4-142,0]) did not differ significantly from the controls (energy intake: 3233kcal ± 1046, p=0,597; protein intake: 103,7g [90,1-125,5], p=0,120). The average difference to the individual calculated KDQOI-guideline was +176,0kcal ± 1156 (p=0,357) for energy intake and -1,75g ± 45,9 (p=0,491) for protein intake. However, there was an observed imbalance in the distribution of macronutrients, with a preference for fats over proteins. The patients’ daily intake of sodium (5,4g [ 2,95-10,1]) was higher than in the controls (4,1g [2,04-5,99], p= 0,058) whereas both values for potassium (3,7g ± 1,84) and phosphorous (1,79g ± 0,91) went significantly below the controls’ values (potassium intake: 4,89g ± 1,74, p=0,014; phosphorous intake: 2,04g ± 0,64, p=0,038). Thus, the values exceeded the calculated KDQOI-recommendation by + 3,3g [0,63-7,90] (p<0,001) for sodium, +1,49g ± 1,84 (p<0,001) for potassium and +0,89g ± 0,91 (p<0,001) for phosphorous. Contrary to the assumption, the patients did not under-eat. Nevertheless, their diets did not align with the recommended values. These findings highlight the need for intervention and education among patients and that regular dietary monitoring could prevent unhealthy nutrition habits. The elaboration of individual references instead of standardized guidelines could increase the compliance to the advised diet so that interdisciplinary comorbidities do not develop or worsen.

Keywords: compliance, dialysis, end-stage renal disease, KDQOI, malnutrition, nutrition guidelines, questionnaire, salt intake

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Authors: Elana Mahboub, Lamar Bakhurji, Hind Alhindi, Sara Alesayi

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Duolingo is a revolutionary language learning platform that offers an interactive and accessible learning experience. Its gamified approach makes language learning engaging and enjoyable, with a diverse range of languages available. The platform's adaptive learning system tailors lessons to individual proficiency levels, ensuring a personalized and efficient learning journey. The incorporation of multimedia elements enhances the learning experience and promotes practical language application. Duolingo's success is attributed to its mobile accessibility, offering basic access to language courses for free, with optional premium features for those seeking additional resources. Research shows positive outcomes for users, and the app's global impact extends beyond individual learning to formal language education initiatives. Duolingo is a transformative force in language education, breaking down barriers and making language learning an attainable goal for millions worldwide.

Keywords: duolingo, artificial intelligence, artificial intelligence in duolingo, benefit of artificial intelligence

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Authors: Daphne Alroy-Thiberge

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Trauma-impacted individuals present unique treatment challenges that include high reactivity, hyper-and hypo-arousal, poor adherence to therapy, as well as powerful transference and counter-transference experiences in therapy. This work provides an overview of the clinical tenets most often encountered in trauma-impacted individuals. Further, it provides readily applicable clinical techniques to optimize therapeutic rapport and facilitate accelerated positive mental health outcomes. Finally, integrated neuroscience and clinical evidence-based data are discussed to shed new light on crisis states in trauma-impacted individuals. This knowledge is utilized to provide effective and concrete interventions towards rapid and successful de-escalation of the impacted individual. A highly interactive, adult-learning-principles-based modality is utilized to provide an organic learning experience for participants. The information and techniques learned aim to increase clinical effectiveness, reduce staff injuries and burnout, and significantly enhance positive mental health outcomes and self-determination for the trauma-impacted individuals treated.

Keywords: clinical competencies, crisis interventions, psychotherapy techniques, trauma informed care

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Authors: Kasif Khan, Azlan Allahwala, Moiz Ali, Hasan Lodhi, Umer Amjad

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The number of runs scored by batsmen and wickets taken by bowlers serves as a natural way of quantifying the performance of a cricketer. Traditionally the batsmen and bowlers are rated on their batting or bowling average respectively. However, in a game like Cricket, it is not sufficient to evaluate performance on the basis of average. The biasness in selecting batsman and bowler on the basis of their past performance. The objective is to predict the best player and comparing their performance on the basis of venue, opponent, weather, and particular position. On the basis of predictions and analysis, and comparison the best team is selected for next upcoming series of Pakistan. The system is based and will be built to aid analyst in finding best possible team combination of Pakistan for a particular match and by providing them with advisories so that they can select the best possible team combination. This will also help the team management in identifying a perfect batting order and the bowling order for each match.

Keywords: data analysis, Pakistan cricket players, quantifying individual performance, cricket

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Authors: Roberta Martino, Viviana Ventre

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Intertemporal choices are choices under conditions of uncertainty in which the consequences are distributed over time. The Discounted Utility Model is the essential reference for describing the individual in the context of intertemporal choice. The model is based on the idea that the individual selects the alternative with the highest utility, which is calculated by multiplying the cardinal utility of the outcome, as if the reception were instantaneous, by the discount function that determines a decrease in the utility value according to how the actual reception of the outcome is far away from the moment the choice is made. Initially, the discount function was assumed to have an exponential trend, whose decrease over time is constant, in line with a profile of a rational investor described by classical economics. Instead, empirical evidence called for the formulation of alternative, hyperbolic models that better represented the actual actions of the investor. Attitudes that do not comply with the principles of classical rationality are termed anomalous, i.e., difficult to rationalize and describe through normative models. The development of behavioral finance, which describes investor behavior through cognitive psychology, has shown that deviations from rationality are due to the limited rationality condition of human beings. What this means is that when a choice is made in a very difficult and information-rich environment, the brain does a compromise job between the cognitive effort required and the selection of an alternative. Moreover, the evaluation and selection phase of the alternative, the collection and processing of information, are dynamics conditioned by systematic distortions of the decision-making process that are the behavioral biases involving the individual's emotional and cognitive system. In this paper we present an original decomposition of the discount function to investigate the psychological principles of hyperbolic discounting. It is possible to decompose the curve into two components: the first component is responsible for the smaller decrease in the outcome as time increases and is related to the individual's impatience; the second component relates to the change in the direction of the tangent vector to the curve and indicates how much the individual perceives the indeterminacy of the future indicating his or her aversion to uncertainty. This decomposition allows interesting conclusions to be drawn with respect to the concept of impatience and the emotional drives involved in decision-making. The contribution that neuroscience can make to decision theory and inter-temporal choice theory is vast as it would allow the description of the decision-making process as the relationship between the individual's emotional and cognitive factors. Neurofinance is a discipline that uses a multidisciplinary approach to investigate how the brain influences decision-making. Indeed, considering that the decision-making process is linked to the activity of the prefrontal cortex and amygdala, neurofinance can help determine the extent to which abnormal attitudes respect the principles of rationality.

Keywords: impatience, intertemporal choice, neurofinance, rationality, uncertainty

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Authors: Gu Pang, Bartosz Gebka

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We forecast the demand of total container throughput at the Indonesia’s largest seaport, Tanjung Priok Port. We propose four univariate forecasting models, including SARIMA, the additive Seasonal Holt-Winters, the multiplicative Seasonal Holt-Winters and the Vector Error Correction Model. Our aim is to provide insights into whether forecasting the total container throughput obtained by historical aggregated port throughput time series is superior to the forecasts of the total throughput obtained by summing up the best individual terminal forecasts. We test the monthly port/individual terminal container throughput time series between 2003 and 2013. The performance of forecasting models is evaluated based on Mean Absolute Error and Root Mean Squared Error. Our results show that the multiplicative Seasonal Holt-Winters model produces the most accurate forecasts of total container throughput, whereas SARIMA generates the worst in-sample model fit. The Vector Error Correction Model provides the best model fits and forecasts for individual terminals. Our results report that the total container throughput forecasts based on modelling the total throughput time series are consistently better than those obtained by combining those forecasts generated by terminal-specific models. The forecasts of total throughput until the end of 2018 provide an essential insight into the strategic decision-making on the expansion of port's capacity and construction of new container terminals at Tanjung Priok Port.

Keywords: SARIMA, Seasonal Holt-Winters, Vector Error Correction Model, container throughput

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Authors: Chryssa Thermolia, Ekaterini S. Bei, Stelios Sotiriadis, Kostas Stravoskoufos, Euripides G. M. Petrakis

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Moving into a new era of healthcare, new tools and devices are developed to extend and improve health services, such as remote patient monitoring and risk prevention. In this concept, Internet of Things (IoT) and Cloud Computing present great advantages by providing remote and efficient services, as well as cooperation between patients, clinicians, researchers and other health professionals. This paper focuses on patients suffering from bipolar disorder, a brain disorder that belongs to a group of conditions called effective disorders, which is characterized by great mood swings.We exploit the advantages of Semantic Web and Cloud Technologies to develop a patient monitoring system to support clinicians. Based on intelligently filtering of evidence-knowledge and individual-specific information we aim to provide treatment notifications and recommended function tests at appropriate times or concluding into alerts for serious mood changes and patient’s non-response to treatment. We propose an architecture, as the back-end part of a cloud platform for IoT, intertwining intelligence devices with patients’ daily routine and clinicians’ support.

Keywords: bipolar disorder, intelligent systems patient monitoring, semantic web technologies, healthcare

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Authors: Zenaide Carvalho da Silva, Leandro Rodrigues Ferreira, Andrey Ricardo Pimentel

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Learning styles (LS) refer to the ways and forms that the student prefers to learn in the teaching and learning process. Each student has their own way of receiving and processing information throughout the learning process. Therefore, knowing their LS is important to better understand their individual learning preferences, and also, understand why the use of some teaching methods and techniques give better results with some students, while others it does not. We believe that knowledge of these styles enables the possibility of making propositions for teaching; thus, reorganizing teaching methods and techniques in order to allow learning that is adapted to the individual needs of the student. Adapting learning would be possible through the creation of online educational resources adapted to the style of the student. In this context, this article presents the structure of a learning object interface adaptation based on the LS. The structure created should enable the creation of the adapted learning object according to the student's LS and contributes to the increase of student’s motivation in the use of a learning object as an educational resource.

Keywords: adaptation, interface, learning object, learning style

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Authors: Tamanna Rimi

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A large body of literature has discussed the determinants of migration decision. However, the potential role of individual risk attitudes on migration decision has so far been overlooked. The research on migration literature has studied how the expected income differential influences migration flows for a risk neutral individual. However, migration takes place when there is no expected income differential or even the variability of income appears as lower than in the current location. This migration puzzle motivates a recent trend in the literature that analyzes how attitudes towards risk influence the decision to migrate. However, the significance of risk attitudes on migration decision has been addressed mostly in a theoretical perspective in the mainstream migration literature. The efficient outcome of labor market and overall economy are largely influenced by migration in many countries. Therefore, attitudes towards risk as a determinant of migration should get more attention in empirical studies. To author’s best knowledge, this is the first study that has examined the relationship between relative risk aversion and migration decision in US market. This paper considers movement across United States as a means of migration. In addition, this paper also explores the network effect due to the increasing size of one’s own ethnic group to a source location on the migration decision and how attitudes towards risk vary with network effect. Two ethnic groups (i.e. Asian and Hispanic) have been considered in this regard. For the empirical estimation, this paper uses two sources of data: 1) U.S. census data for social, economic, and health research, 2010 (IPUMPS) and 2) University of Michigan Health and Retirement Study, 2010 (HRS). In order to measure relative risk aversion, this study uses the ‘Two Sample Two-Stage Instrumental Variable (TS2SIV)’ technique. This is a similar method of Angrist (1990) and Angrist and Kruegers’ (1992) ‘Two Sample Instrumental Variable (TSIV)’ technique. Using a probit model, the empirical investigation yields the following results: (i) risk attitude has a significantly large impact on migration decision where more risk averse people are less likely to migrate; (ii) the impact of risk attitude on migration varies by other demographic characteristics such as age and sex; (iii) people with higher concentration of same ethnic households living in a particular place are expected to migrate less from their current place; (iv) the risk attitudes on migration vary with network effect. The overall findings of this paper relating risk attitude, migration decision and network effect can be a significant contribution addressing the gap between migration theory and empirical study in migration literature.

Keywords: migration, network effect, risk attitude, U.S. market

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Authors: Ousmane Youme, Jean Marie Dembele, Eugene Ezin, Christophe Cambier

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In recent years, the convolutional neural networks (CNN) architectures designed by evolution algorithms have proven to be competitive with handcrafted architectures designed by experts. However, these algorithms need a lot of computational power, which is beyond the capabilities of most researchers and engineers. To overcome this problem, we propose an evolution architecture under length constraints. It consists of two algorithms: a search length strategy to find an optimal space and a search architecture strategy based on a genetic algorithm to find the best individual in the optimal space. Our algorithms drastically reduce resource costs and also keep good performance. On the Cifar-10 dataset, our framework presents outstanding performance with an error rate of 5.12% and only 4.6 GPU a day to converge to the optimal individual -22 GPU a day less than the lowest cost automatic evolutionary algorithm in the peer competition.

Keywords: CNN architecture, genetic algorithm, evolution algorithm, length constraints

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Authors: Mohamed Elhadi M. Sharif, Mona Masood

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The usage of e-health facilities is seen to be the first priority by the Libyan government. As such, this paper focuses on how the key factors or elements of working size in terms of technological availability, structural environment, and other competence-related matters may affect nurses’ sharing of knowledge in e-health. Hence, this paper investigates learning readiness assessment to raise e-health for Libyan regional hospitals by using e-health services in nursing education.

Keywords: Libyan nurses, e-learning readiness, e-health, nursing education

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Authors: Stijn Horck

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Background: The value of lessons learned from failure increases when these insights can be put to use by those who did not experience the failure. While learning from others has mostly been researched between individuals or teams within the same environment, transferring knowledge from the person who experienced the failure to an outsider comes with extra challenges. As sense-making of failure is an individual process leading to different learning experiences, the potential of lessons learned from failure is highly variable depending on who is transferring the lessons learned. Using an integrated framework of linguistic aspects related to attributional egotism, this study aims to offer a complete explanation of the challenges in transferring lessons learned from failures that are experienced by others. Method: A case study of a failed foundation established to address the information needs for GPs in times of COVID-19 has been used. An overview of failure causes and lessons learned were made through a preliminary analysis of data collected in two phases with metaphoric examples of failure types. This was followed up by individual narrative interviews with the board members who have all experienced the same events to analyse the individual variance of lessons learned through discourse analysis. This research design uses the researcher-as-instrument approach since the recipient of these lessons learned is the author himself. Results: Thirteen causes were given why the foundation has failed, and nine lessons were formulated. Based on the individually emphasized events, the explanation of the failure events mentioned by all or three respondents consisted of more linguistic aspects related to attributional egotism than failure events mentioned by only one or two. Moreover, the learning events mentioned by all or three respondents involved lessons learned that are based on changed insight, while the lessons expressed by only one or two are more based on direct value. Retrospectively, the lessons expressed as a group in the first data collection phase seem to have captured some but not all of the direct value lessons. Conclusion: Individual variance in expressing lessons learned to outsiders can be reduced using metaphoric or analogical explanations from a third party. In line with the attributional egotism theory, individuals separated from a group that has experienced the same failure are more likely to refer to failure causes of which the chances to be contradicted are the smallest. Lastly, this study contributes to the academic literature by demonstrating that the use of linguistic analysis is suitable for investigating the knowledge transfer from lessons learned after failure.

Keywords: failure, discourse analysis, knowledge transfer, attributional egotism

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Authors: Shishen Xie, Yingda L. Xie

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Tuberculosis (TB) is a potentially serious infectious disease that remains a health concern. The Interferon Gamma Release Assay (IGRA) is a blood test to find out if an individual is tuberculous positive or negative. This study applies statistical analysis to the clinical data of interferon-gamma levels of seventy-three subjects who diagnosed pulmonary TB in an anti-tuberculous treatment. Data analysis is performed to determine if there is a significant decline in interferon-gamma levels for the subjects during a period of six months, and to infer if the anti-tuberculous treatment is effective.

Keywords: data analysis, interferon gamma release assay, statistical methods, tuberculosis infection

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Authors: Jiri Beranovsky, Jaroslav Knapek, Tomas Kralik, Kamila Vavrova

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The paper deals with the results of research focused on the complex aspects of the use of intentionally grown biomass on agricultural land for the production of solid biofuels as an alternative for individual household heating. . The study primarily deals with the analysis of CO2 emissions of the logistics cycle of biomass for the production of energy pellets. Growing, harvesting, transport and storage are evaluated in the pellet production cycle. The aim is also to take into account the consumption profile during the year in terms of heating of common family houses, which are typical end-market segment for these fuels. It is assumed that in family houses, bio-pellets are able to substitute typical fossil fuels, such as brown coal and old wood burning heating devices and also electric boilers. One of the competing technology with the pellets are heat pumps. The results show the CO2 emissions related with considered fuels and technologies for their utilization. Comparative analysis is aimed biopellets from intentionally grown biomass, brown coal, natural gas and electricity used in electric boilers and heat pumps. Analysis combines CO2 emissions related with individual fuels utilization with costs of these fuels utilization. Cost of biopellets from intentionally grown biomass is derived from the economic models of individual energy crop plantations. At the same time, the restrictions imposed by EU legislation on Ecodesign's fuel and combustion equipment requirements and NOx emissions are discussed. Preliminary results of analyzes show that to achieve the competitiveness of pellets produced from specifically grown biomass, it would be necessary to either significantly ecological tax on coal (from about 0.3 to 3-3.5 EUR/GJ), or to multiply the agricultural subsidy per area. In addition to the Czech Republic, the results are also relevant for other countries, such as Bulgaria and Poland, which also have a high proportion of solid fuels for household heating.

Keywords: CO2 emissions, heating costs, energy crop, pellets, brown coal, heat pumps, economical evaluation

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Authors: Richard Ren

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Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

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Authors: Anna Wszołek

Abstract:

For a very long time, the doctor-patient relationship had a paternalistic character. The events of the II World War, as well as fast development of the biotechnology and medicine caused an important change in that relationship. Human beings and their dignity were put in the centre of philosophical and legal debate. The increasing frequency of clinical trials led to the emergence of bioethics, which dealt with the topic of the possibilities and boundaries of such research in relation to individual’s autonomy. Thus, there was a transformation from a paternalistic relationship to a more collaborative one in which the patient has more room for self-determination. Today, patients are more and more aware of their rights and the obligations placed on doctors and the health care system, which is linked to an increase in medical malpractice claims. Unfortunately, these claims are not always justified. There is a strong concentration around the topic of patient’s good, however, at the other side there are doctors who feel, on the example of Poland, they might be easily accused and sued for medical malpractice even though they fulfilled their duties. Such situation may have a negative impact on the quality of health care services and patient’s interests. This research is going to present doctor’s perspective on the topic of medical malpractice allegations. It is supposed to show possible damage to a doctor’s reputation caused by frivolous and weakly justified medical malpractice accusations, as well as means to protect this reputation.

Keywords: doctor's reputation, medical malpractice, personal rights, unjustified allegations

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Authors: Mei Li, Victor Meddalena

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Background: Newfoundland and Labrador (NL) has rapidly aging population and is characterized by its vast geography with high proportion of dispersed rural communities when compared to other provinces in Canada. Structural, demographic and geographic factors have created big gaps for rural residents across NL with respect to accessing various health and social services. While the barriers are well documented for patients’ access to cancer care in rural and remote areas, challenges faced by family caregivers are not fully recognized. Caregiving burden coupled with challenges associated with relocation and frequent travels create situations where caregivers are vulnerable physically, emotionally, financially and socially. This study examines the experiences of family caregivers living in rural NL through a social justice lens. It is expected to identify the gaps existing in social policy and support for rural family caregivers. It will make a novel contribution to the literature in this regard. Methods: Design: This qualitative study adopted the hermeneutic phenomenology to best describe and interpret rural-based family caregivers’ living experiences and explore the meaning, impact, and the influence of both individual experience and contextual factors shaping these experiences. Data Collection: In-depth interviews with key informants were conducted with 12 participants from various rural communities in NL. A case study was also used to explore an individual’s experience in complex social units consisting of multiple variables of in-depth understanding of the reality. Data Analysis: Thematic analysis guided by the Voice-Centred Relational (VCR) method was employed to explore the relationships and contexts of participants. Emerging Themes: Six major emerging themes were identified, namely, overwhelming caregiving burden on rural family caregivers, long existing financial hardship, separation from family and community, low level of social support and self-reliance coping strategies, and social vulnerability and isolation. Conclusion: Understanding the lived experiences of rural-based family caregivers is critical to inform the policy makers the gap of health and social service in NL. The findings of this study also have implications for family caregivers who are vulnerable in other similar contexts. This study adds innovative insights for policy making and service provision in this regard.

Keywords: family caregivers, policy, relocation, rural

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Authors: Luiz Drude de Lacerda, Kevin Luiz Cordeiro Ferrer do Carmo, Victor Lacerda Moura, Rayone Wesley Santos de Oliveira, Moisés Fernandes Bezerra

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Fish is a food source well recognized for its health benefits. However, the consumption of fish, especially carnivorous species, is the main path of human exposure to Hg, a widely distributed pollutant on the planet and that accumulates along food chains. Studies on the impacts on public health by fish intake show existing toxic risks even when at low concentrations. This study quantifies, for the first time, the concentrations of Hg in muscle tissue of the nine most commercialized fish species in the fish market of São Luís (MA) in north Brazil and estimates the consequent human exposure through consumption. Concentrations varied according to trophic level, with the highest found in the larger carnivorous species; the Yellow hake (Cynoscion acoupa) (296.4 ± 241.2 ng/g w.w) and the Atlantic croaker (Micropogonias undulatus) (262.8 ± 89.1 ng/g w.w.), whereas the lowest concentrations were recorded in iliophagous Mullets (Mugil curema) (20.5 ± 9.6 ng/g w.w.). Significant correlations were observed between Hg concentrations and individual length in only two species: the Flaming catfish (Bagre marinus) and the Atlantic bumper (Chloroscombrus crysurus). Given the relatively uniform size of individuals of the other species and/or the small number of samples, this relationship was not found for the other species. The estimated risk coefficients, despite the relatively low concentrations of Hg, suggest that yellow hake and Whitemouth croaker (Micropogonias furnieri), fish most consumed by the local population, present some risk to human health (> 1) HQ and THQ, depending on the frequency of their consumption.

Keywords: contamination, fish, human exposure, risk assessment

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Authors: Mohammed Benlemlih

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Using a sample of 22,839 US firm-year observations over the 1991-2012 period, we find that high CSR firms pay more dividends than low CSR firms. The analysis of individual components of CSR provides strong support for this main finding: five of the six individual dimensions are also associated with high dividend payout. When analyzing the stability of dividend payout, our results show that socially irresponsible firms adjust dividends more rapidly than socially responsible firms do: dividend payout is more stable in high CSR firms. Additional results suggest that firms involved in two controversial activities -the military and alcohol - are associated with low dividend payouts. These findings are robust to alternative assumptions and model specifications, alternative measures of dividend, additional control, and several approaches to address endogeneity. Overall, our results are consistent with the expectation that high CSR firms may use dividend policy to manage the agency problems related to overinvestment in CSR.

Keywords: corporate social responsibility, dividend policy, Lintner model, agency theory, signaling theory, dividend stability

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Authors: Nuruddeen Abubakar Adamu

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Background: Global health nursing describes health-related work across borders and focuses more on the differences between the nurses’ role between countries and identified why nursing care in particular country differs from another. It also helps in analyzing the health issues and concerns that transcend national borders class, race, ethnicity and culture. The primary objective of this study is to introduce the concept of global health nursing. And the article also argues for the need for global health nursing. Methods This review assesses available evidence, both published and unpublished, on issues relating to the global health nursing and the nurse's role in global health. The review is qualitative based. Results: Globalization, modern technologies, travel, migration and changes in diseases trend globally has made the nursing role to become more diverse and less traditional. These issues change the nurse’s role in the healthcare industry to become enormous and very challenging. This article considers response to issues of emerging global health nursing concept, challenges, purposes, global health nursing activities in both developed and developing countries and the nurse's role globally in maternal-newborn health; preparedness for advocacy in global health within a framework of social justice, equity; and health system strengthening globally. Conclusion: Global health nursing goes beyond the intervention to care for a patient with a particular health problem but, however health is interconnected to political, economic and social context and therefore this explains the need of a multi-professional and multi-sectoral approach to achieve the goal of global health and the need for global health nursing. Global health equity can be promoted and if the profile of nursing and nurses will be raised and enable nurses to be aware of global health issues so as to enable them to work to their full maximum potential, to attain greater health outcome and wellness.

Keywords: global health nursing, double burden of diseases, globalization, health equity

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Authors: Joane Adeclas, Yoonyoung Kim, Taekyun Hur

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This study investigates individual’s intrinsic motivation to practice their leisure activities, as well as, how the cultural environment may influence their motivation to practice their activities. Focused on the positive psychology, the present study proposed redefinition of leisure activities considering two factors. First, leisure activities could be as any activities that provide pleasure or meaning to individuals. Second, they can be practiced alone or in groups. In fact, based on this definition, a four-dimensional model of leisure activities was developed, to measure individual’s perception of their leisure experience, based on four factors that are: personal pleasure, social pleasure, personal meaning and social meaning. Furthermore, recent studies have argued that leisure activities can be interpreted and understood differently across cultures. Therefore, the present study proposed to examine the possible role of the cultural context of individual’s leisure practices. To do so, two cultural groups (Koreans vs. French) were compared in terms of the four-dimensional model of leisure activities. Three hundred Koreans and three hundred French participants were asked to answer an online survey about their leisure activities. Participants had to respond to questions related to several aspects of leisure practices as followed: the reason why their practice their leisure activities, the reason why they fail to practice their leisure, and their obsession relate to their leisure activities. Factor analyses based on participant’s responses proposed a moderate fit of the four-dimensional model of leisure activities. Furthermore, significant cultural differences were also found. As a result, the cultural context seems to influence the reason why individuals practice their leisure activities based on our model. In fact, Koreans explained more than French, the practice of their leisure activities with social-pleasurable reasons. At a contrary, French explained more than Koreans, the practice of their leisure activities with social-meaningful reasons. The two cultural groups also significantly differ on their perception of failure. The results showed that French participants used more meaningful social factors to explain why they failed to practice their leisure activities than did Koreans participants. Finally, Koreans and French significantly differed regarding their obsession on their leisure activities. In general, French tend to have more obsession than Koreans about their leisure activities. Those results validated the four-dimensional model of leisure, as well as, the cultural differences in leisure practices. However, further studies are needed to validate this model at an individual and cultural level.

Keywords: culture, leisure, meaning, pleasure

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Authors: Maria Do Carmo Baracho De Alencar

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Back pain is considered a worldwide public health problem and has led to numerous work-related absence from work and public spending on rehabilitation, as well as difficulties in the process of professional rehabilitation and return to work. Also, the rehabilitation of workers is one of the great challenges today and for the field of Workers' Health in Brazil. Aim: To investigate the procedures related to the professional rehabilitation of insured workers affected by chronic low back pain, based on the perceptions of professional counselors. Methods: A list of related professional counselors was obtained from the Professional Rehabilitation Coordination of the Baixada Santista (SP) region, and from the Social Security National Institute of Brazil, and in which cities they worked. Semistructured and individual interview was scheduled, based on a pre-elaborated script, containing questions about procedures, experiences at work and feelings. The interviews were recorded and transcribed in full for content analysis. Results: Ten (10) professional counselors of both genders and from nine (9) cities from the Baixada Santista region participated in the study. Aged between 31 and 64 years, and time in service between 4 and 38 years. Only one of the professionals was graduaded in Psychology. Among the testimonies emerged the high demand of work, the lack of interest of companies, medical authority, the social helplessness after rehabilitation process, difficulty in assessing invisible pain, and suffering, anguish, and frustration at work, between others. Conclusion: The study contributes to reflections about the importance of interdisciplinary actions and the Psychology in the processes of professional rehabilitation and readaptation in the process of return to work.

Keywords: low back pain, rehabilitation, work, occupational health

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Authors: Richard Ssempala, Francis Kintu, Christine K. Tashobya

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Funding for health remains a key constraint facing many developing countries, Uganda inclusive. Uganda’s health sector budget to the national budgetary allocation has stagnated between 8.2% to 10% over the years. Using data collected from different government documents, we sought to establish the implications of the budget allocation over the period (FY2010/11-2018/19) on health services delivery in Uganda to inform policymakers specifically Members of Parliament who are critical in making sectorial allocation on the steps they can adapt to change the terrain of health financing in Uganda. Findings revealed that the contribution of public funding to the health sector is low (15.7%) with private sources (42.6%) and donors contributing much more, with the bulk of private funds, are out of pocket. The study further revealed that low budget allocation had been manifested in inadequate and poorly motivated health workers, essential drug stock-outs that ultimately contribute to poor access to services, catastrophic health expenditures, and high morbidity rates. We recommend for a substantial and sustained increase in the government health budget, optimizing the available resources by addressing wastages, prioritizing health promotion, prevention and finally, institutionalizing the National Health Insurance Scheme.

Keywords: budget allocations, universal health coverage, health service delivery, Uganda

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Authors: Jhansi Rani Boda, G. Sunitha

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Worldwide, the financial markets are influenced by several factors such as the changes in economic and political processes that occur in the country and the globe, information diffusion and approachability and so on. Yet, the foremost important factor is the investor’s reaction and perception. For an individual investor, decision-making process can be perceived as a continuous process that has significant impact of their psychology while making investment decisions. Behavioral finance relies on research of human and social recognition and emotional tolerance studies to identify and understand the investment decisions. This article aims to report the research of individual investor’s financial behavior in a historical perspective. This article uncovers the investor’s psychology in investment decision making focusing on the investor’s rationality with an explanation of psychological and emotional factors that affect investing. The results of the study are revealed by means of Graphical visualization.

Keywords: behavioral finance, psychology, investor’s behavior, psychological and emotional factors

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Authors: Ashish Dhamaniya, Satish Chandra

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Speed and traffic volume data are collected on different sections of four lane and six lane roads in three metropolitan cities in India. Speed data are analyzed to fit the statistical distribution to individual vehicle speed data and all vehicles speed data. It is noted that speed data of individual vehicle generally follows a normal distribution but speed data of all vehicle combined at a section of urban road may or may not follow the normal distribution depending upon the composition of traffic stream. A new term Speed Spread Ratio (SSR) is introduced in this paper which is the ratio of difference in 85^th and 50^th percentile speed to the difference in 50^th and 15^th percentile speed. If SSR is unity then speed data are truly normally distributed. It is noted that on six lane urban roads, speed data follow a normal distribution only when SSR is in the range of 0.86 – 1.11. The range of SSR is validated on four lane roads also.

Keywords: normal distribution, percentile speed, speed spread ratio, traffic volume

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Authors: Sancho-Esper Franco, Rodríguez Sánchez Carla, Sánchez Carolina, Orús-Sanclemente Carlos

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Water scarcity is a reality that affects many regions of the world and is aggravated by climate change and population growth. Saving water has become an urgent need to ensure the sustainability of the planet and the survival of many communities, where youth and social networks play a key role in promoting responsible practices and adopting habits that contribute to environmental preservation. This study analyzes the persuasion capacity of messages designed to promote pro-environmental behaviors among youth. Specifically, it studies how the efficacy (effectiveness) of the response (personal response efficacy/effectiveness) and the perception of distance from the source of the message influence the water-saving behavior of the audience. To do so, two communication frameworks are combined. First, the Construal Level Theory, which is based on the concept of "psychological distance", that is, people, objects or events can be perceived as psychologically near or far, and this subjective distance (i.e., social, temporal, or spatial) determines their attitudes, emotions, and actions. This perceived distance can be social, temporal, or spatial. This research focuses on studying the spatial distance and social distance generated by cultural differences between influencers and their audience to understand how cultural distance can influence the persuasiveness of a message. Research on the effects of psychological distance between influencers-followers in the pro-environmental field is very limited, being relevant because people could learn specific behaviors suggested by opinion leaders such as influencers in social networks. Second, different approaches to behavioral change suggest that the perceived efficacy of a behavior can explain individual pro-environmental actions. People will be more likely to adopt a new behavior if they perceive that they are capable of performing it (efficacy belief) and that their behavior will effectively contribute to solving that problem (personal response efficacy). It is also important to study the different actors (social and individual) that are perceived as responsible for addressing environmental problems. Specifically, we analyze to what extent the belief individual’s water-saving actions are effective in solving the problem can influence water-saving behavior since this individual effectiveness increases people's sense of obligation and responsibility with the problem. However, in this regard, empirical evidence presents mixed results. Our study addresses the call for experimental studies manipulating different subtypes of response effectiveness to generate robust causal evidence. Based on all the above, this research analyzes whether cultural distance (local vs. international influencer) and the perception of effectiveness of behavior (personal response efficacy) (personal/individual vs. collective) affect the actual behavior and the intention to conserve water of social network users. An experiment of 2 (local influencer vs. international influencer) x 2 (effectiveness of individual vs. collective response) is designed and estimated. The results show that a message from a local influencer appealing to individual responsibility exerts greater influence on intention and actual water-saving behavior, given the cultural closeness between influencer-follower, and the appeal to individual responsibility increases the feeling of obligation to participate in pro-environmental actions. These results offer important implications for social marketing campaigns that seek to promote water conservation.

Keywords: social marketing, influencer, message framing, experiment, personal response efficacy, water saving

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