Search results for: health-care workers

Authors: Nizamuddin Zainuddin, Adam Mohd Saifudin, Ahmad Yusni Bahaudin, Mohd Hanizan Zalazilah, Roslan Jamaluddin

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Students’ enhanced experience through simulation is a crucial factor that brings reality to the classroom. The enhanced experience is all about developing, enriching and applications of a generic process flow in the field of logistics and transportations. As educational technology has improved, the effective use of simulations has greatly increased to the point where simulations should be considered a valuable, mainstream pedagogical tool. Additionally, in this era of ongoing (some say never-ending) assessment, simulations offer a rich resource for objective measurement and comparisons. Simulation is not just another in the long line of passing fads (or short-term opportunities) in educational technology. It is rather a real key to helping our students understand the world. It is a way for students to acquire experience about how things and systems in the world behave and react, without actually touching them. In short, it is about interactive pretending. Simulation is all about representing the real world which includes grasping the complex issues and solving intricate problems. Therefore, it is crucial before stimulate the real process of inbound and outbound logistics and transportation a generic process flow shall be developed. The paper will be focusing on the validization of the process flow by looking at the inputs gains from the sample. The sampling of the study focuses on multi-national and local manufacturing companies, third party companies (3PL) and government agency, which are selected in Peninsular Malaysia. A simulation flow chart was proposed in the study that will be the generic flow in logistics and transportation. A qualitative approach was mainly conducted to gather data in the study. It was found out from the study that the systems used in the process of outbound and inbound are System Application Products (SAP) and Material Requirement Planning (MRP). Furthermore there were some companies using Enterprises Resources Planning (ERP) and Electronic Data Interchange (EDI) as part of the Suppliers Own Inventories (SOI) networking as a result of globalized business between one countries to another. Computerized documentations and transactions were all mandatory requirement by the Royal Custom and Excise Department. The generic process flow will be the basis of developing a simulation program that shall be used in the classroom with the objective of further enhanced the students’ learning experience. Thus it will contributes to the body of knowledge on the enrichment of the student’s employability and also shall be one of the way to train new workers in the logistics and transportation filed.

Keywords: enhancement, simulation, process flow, logistics, transportation

Procedia PDF Downloads 326

Authors: Bi Xiao Fang, Tan Bao Zhen

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Soft skills, core competencies, and generic competencies are exchangeable terminologies often used to represent a similar concept. In the Singapore context, such skills are currently being referred to as Critical Core Skills (CCS). In 2019, SkillsFuture Singapore (SSG) reviewed the Generic Skills and Competencies (GSC) framework that was first introduced in 2016, culminating in the development of the Critical Core Skills (CCS) framework comprising 16 soft skills classified into three clusters. The CCS framework is part of the Skills Framework, and whose stated purpose is to create a common skills language for individuals, employers and training providers. It is also developed with the objectives of building deep skills for a lean workforce, enhance business competitiveness and support employment and employability. This further helps to facilitate skills recognition and support the design of training programs for skills and career development. According to SSG, every job role requires a set of technical skills and a set of Critical Core Skills to perform well at work, whereby technical skills refer to skills required to perform key tasks of the job. There has been an increasing emphasis on soft skills for the future of work. A recent study involving approximately 80 organizations across 28 sectors in Singapore revealed that more enterprises are beginning to recognize that soft skills support their employees’ performance and business competitiveness. Though CCS is of high importance for the development of the workforce’s employability, there is little attention paid to the CCS use and profiling across occupations. A better understanding of how CCS is distributed across the economy will thus significantly enhance SSG’s career guidance services as well as training providers’ services to graduates and workers and guide organizations in their hiring for soft skills. This CCS profiling study sought to understand how CCS is demanded in different occupations. To achieve its research objectives, this study adopted a quantitative method to measure CCS use across different occupations in the Singaporean workforce. Based on the CCS framework developed by SSG, the research team adopted a formative approach to developing the CCS profiling tool to measure the importance of and self-efficacy in the use of CCS among the Singaporean workforce. Drawing on the survey results from 2500 participants, this study managed to profile them into seven occupation groups based on the different patterns of importance and confidence levels of the use of CCS. Each occupation group is labeled according to the most salient and demanded CCS. In the meantime, the CCS in each occupation group, which may need some further strengthening, were also identified. The profiling of CCS use has significant implications for different stakeholders, e.g., employers could leverage the profiling results to hire the staff with the soft skills demanded by the job.

Keywords: employability, skills profiling, skills measurement, soft skills

Procedia PDF Downloads 90

Authors: Marita Heyns

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Job insecurity is a distressing phenomenon which has far reaching consequences for both employees and their organizations. Previously, much attention has been given to the link between job insecurity and individual level performance outcomes, while less is known about how subjectively perceived job insecurity might transfer beyond the individual level to affect performance of the organization on an aggregated level. Research focusing on how employees’ fear of job loss might affect the organization’s ability to respond proactively to volatility and drastic change through applying its capabilities of sensing, seizing, and reconfiguring, appears to be practically non-existent. Equally little is known about the potential underlying mechanisms through which job insecurity might affect the dynamic capabilities of an organization. This study examines how job insecurity might affect dynamic organizational capability through trust as an underling process. More specifically, it considered the simultaneous roles of trust at an impersonal (organizational) level as well as trust at an interpersonal level (in leaders and co-workers) as potential underlying mechanisms through which job insecurity might affect the organization’s dynamic capability to respond to opportunities and imminent, drastic change. A quantitative research approach and a stratified random sampling technique enabled the collection of data among 314 managers at four different plant sites of a large South African steel manufacturing organization undergoing dramatic changes. To assess the study hypotheses, the following statistical procedures were employed: Structural equation modelling was performed in Mplus to evaluate the measurement and structural models. The Chi-square values test for absolute fit as well as alternative fit indexes such as the Comparative Fit Index and the Tucker-Lewis Index, the Root Mean Square Error of Approximation and the Standardized Root Mean Square Residual were used as indicators of model fit. Composite reliabilities were calculated to evaluate the reliability of the factors. Finally, interaction effects were tested by using PROCESS and the construction of two-sided 95% confidence intervals. The findings indicate that job insecurity had a lower-than-expected detrimental effect on evaluations of the organization’s dynamic capability through the conducive buffering effects of trust in the organization and in its leaders respectively. In contrast, trust in colleagues did not seem to have any noticeable facilitative effect. The study proposes that both job insecurity and dynamic capability can be managed more effectively by also paying attention to factors that could promote trust in the organization and its leaders; some practical recommendations are given in this regard.

Keywords: dynamic organizational capability, impersonal trust, interpersonal trust, job insecurity

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Authors: Nikhil A. Gavhane, Sachin Shah, Ishant S. Mahajan, Pawan D. Bahekar

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Introduction: Millions of people are affected with dengue fever every year, which drives up healthcare expenses in many low-income countries. Organ failure and other serious symptoms may result. Another worldwide public health problem is sickle cell anaemia, which is most prevalent in Africa, the Caribbean, and Europe. Dengue epidemics have reportedly occurred in locations with a high frequency of sickle cell disease, compounding the health problems in these areas. Aims and Objectives: This study examines dengue infection in sickle cell disease-afflicted pre-schoolers. Method:This Retrospective cohort study examined paediatric patients. Young people with sickle cell disease (SCD), dengue infection, and a control group without SCD or dengue were studied. Data on demographics, SCD consequences, medical treatments, and laboratory findings were gathered to analyse the influence of SCD on dengue severity and clinical outcomes, classified as severe or non-severe by the 2009 WHO classification. Using fever or admission symptoms, the research estimated acute illness duration. Result: Table 1 compares haemoglobin genotype-based dengue episode features in SS, SC, and controls. Table 2 shows that severe dengue cases are older, have longer admission delays, and have particular symptoms. Table 3's multivariate analysis indicates SS genotype's high connection with severe dengue, multiorgan failure, and acute pulmonary problems. Table 4 relates severe dengue to greater white blood cell counts, anaemia, liver enzymes, and reduced lactate dehydrogenase. Conclusion: This study is valuable but confined to hospitalised dengue patients with sickle cell illness. Small cohorts limit comparisons. Further study is needed since findings contradict predictions.

Keywords: dengue, chills, headache, severe myalgia, vomiting, nausea, prostration

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Authors: James Ladzekpo

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Background: The urgent need to identify new pharmacological targets for diabetes treatment and prevention has been amplified by the disease's extensive impact on individuals and healthcare systems. A deeper insight into the biological underpinnings of diabetes is crucial for the creation of therapeutic strategies aimed at these biological processes. Current predictive models based on genetic variations fall short of accurately forecasting diabetes. Objectives: Our study aims to pinpoint key epigenetic factors that predispose individuals to diabetes. These factors will inform the development of an advanced predictive model that estimates diabetes risk from genetic profiles, utilizing state-of-the-art statistical and data mining methods. Methodology: We have implemented a recursive feature elimination with cross-validation using the support vector machine (SVM) approach for refined feature selection. Building on this, we developed six machine learning models, including logistic regression, k-Nearest Neighbors (k-NN), Naive Bayes, Random Forest, Gradient Boosting, and Multilayer Perceptron Neural Network, to evaluate their performance. Findings: The Gradient Boosting Classifier excelled, achieving a median recall of 92.17% and outstanding metrics such as area under the receiver operating characteristics curve (AUC) with a median of 68%, alongside median accuracy and precision scores of 76%. Through our machine learning analysis, we identified 31 genes significantly associated with diabetes traits, highlighting their potential as biomarkers and targets for diabetes management strategies. Conclusion: Particularly noteworthy were the Gradient Boosting Classifier and Multilayer Perceptron Neural Network, which demonstrated potential in diabetes outcome prediction. We recommend future investigations to incorporate larger cohorts and a wider array of predictive variables to enhance the models' predictive capabilities.

Keywords: diabetes, machine learning, prediction, biomarkers

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Authors: Ouassila Bekkal Brikci Benhabib, Zahia Boucherit Otmani, Kebir Boucherit, A. Seghir

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The establishment of intravenous catheters in hospitalized patient is an act common in many clinical situations. These therapeutic tools, from their insertion in the body, represent gateways including fungal germs prone. The latter can generate the growth of biofilms, which can be the cause of fungal infection. Faced with this problem, we conducted a study at the University Hospital of Tlemcen in the neurosurgery unit and aims to isolate and identify Candida yeasts from intravenous catheters. Then test their ability to form biofilms. Materials and methods: 256 patient hospitalized in surgery of the hospital in west Algeria were submitted to this study. All samples were taken from peripheral venous catheters implanted for 72 hours or more days. A total of 31 isolates of Candida species were isolated. MIC and SMIC are determined at 80% inhibition by the test XTT tetrazolium measured at 490 nm. The final concentrations of antifungal agent being between 0.03 and 16 mg / ml for amphotericin B and from 0.015 to 8 mg / mL caspofungin. Results: 31 Candida species isolates from catheters including 14 Candida albicans and 17 Candida non albicans . 21 strains of all the isolates were able to form biofilms. In their form of Planktonic cells, all isolates are 100% susceptible to antifungal agents tested. However, in their state of biofilms, more isolates have become tolerant to the tested antifungals. Conclusion: Candida yeasts isolated from intravascular catheters are considered an important virulence factor in the pathogenesis of infections. Their involvement in catheter-related infections can be disastrous for their potential to generate biofilms. They survive high concentrations of antifungal where treatment failure. Pending the development of a therapeutic approach antibiofilm related to catheters, their mastery is going through: -The risk of infection prevention based on the training and awareness of medical staff, -Strict hygiene and maximum asepsis, and -The choice of material limiting microbial colonization.

Keywords: candida, biofilm, hospital, infection, amphotericin B, caspofungin

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Authors: Maria Karidakis, Barbara Kelly

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The challenges precipitated by the advent of COVID-19 have brought to the fore the task governments and key stakeholders are faced with; ensuring public health communication is readily accessible to vulnerable populations. COVID-19 has presented challenges for the provision and reception of timely, accessible, and accurate health information pertaining to vaccine health messaging to Aboriginal and Torres Strait Islander peoples. The aim of this qualitative study was to explore strategies used by Aboriginal-led organisations to improve communication about COVID-19 and vaccination for their communities and to explore how these mediation and outreach strategies were received by community members. We interviewed 6 Aboriginal-led organisations and 15 community members from several states across Australian, and these interviews were analysed thematically. The findings suggest that effective public health communication is enhanced when aFirst nations-led response defines the governance that happens in First Nations communities. Pro-active and self-determining Aboriginal leadership and decision-making helps drive the response to counter a growing trend towards vaccine hesitancy. Other strategies include establishing partnerships with government departments and relevant non-governmental organisations to ensure services are implemented and culturally appropriate. The outcomes of this research will afford policymakers, stakeholders in healthcare, and cultural mediators the capacity to identify strengths and potential problems associated with pandemic health information and to subsequently implement creative and culturally specific solutions that go beyond the provision of written documentation via translation or interpreting. It will also enable governing bodies to adjust multilingual polices and to adopt mediation strategies that will improve information delivery and intercultural services on a national and international level.

Keywords: intercultural communication, qualitative, public health communication, COVID-19, pandemic, mediated communication, first nations people

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Authors: Stefanache Stefania, Ioana R. Podina

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Background: Embodied Conversational Agents (ECAs) are revolutionizing education and healthcare by offering cost-effective, adaptable, and portable solutions. Research on the Uncanny Valley effect (UVE) involves various embodied agents, including ECAs. Achieving the optimal level of anthropomorphism, no consensus on how to overcome the uncanniness problem. Objectives: This systematic review aims to identify the user characteristics, agent features, and context factors that influence the UVE. Additionally, this review provides recommendations for creating effective ECAs and conducting proper experimental studies. Methods: We conducted a systematic review following the PRISMA 2020 guidelines. We included quantitative, peer-reviewed studies that examined human-ECA interaction. We identified 17,122 relevant records from ACM Digital Library, IEE Explore, Scopus, ProQuest, and Web of Science. The quality of the predictors, mediators, and moderators adheres to the guidelines set by prior systematic reviews. Results: Based on the included studies, it can be concluded that females and younger people perceive the ECA as more attractive. However, inconsistent findings exist in the literature. ECAs characterized by extraversion, emotional stability, and agreeableness are considered more attractive. Facial expressions also play a role in the UVE, with some studies indicating that ECAs with more facial expressions are considered more attractive, although this effect is not consistent across all studies. Few studies have explored contextual factors, but they are nonetheless crucial. The interaction scenario and exposure time are important circumstances in human-ECA interaction. Conclusions: The findings highlight a growing interest in ECAs, which have seen significant developments in recent years. Given this evolving landscape, investigating the risk of the UVE can be a promising line of research.

Keywords: human-computer interaction, uncanny valley effect, embodied conversational agent, systematic review

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Authors: Ana Mafalda Silva, Rebeca Fontes, Ana Paula Vaz, Carla Carreira, Ana Corte-Real

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Decades of research on the topic of interpersonal violence against minors highlight five main conclusions: 1) it causes harmful effects on children's development and health; 2) it is prevalent; 3) it violates children's rights; 4) it can be prevented and 5) parents are the main aggressors. The child abuse scenario is identified through clinical observation, administrative data and self-reports. The most used instruments are self-reports; however, there are no valid and reliable self-report instruments for minors, which consist of a retrospective interpretation of the situation by the victim already in her adult phase and/or by her parents. Clinical observation and collection of information, namely from the orofacial region, are essential in the early identification of these situations. The management of medical data, such as personal data, must comply with the General Data Protection Regulation (GDPR), in Europe, and with the General Law of Data Protection (LGPD), in Brazil. This review aims to answer the question: In a situation of medical assistance to minors, in the suspicion of interpersonal violence, due to mistreatment, is it necessary for the guardians to provide consent in the registration and sharing of personal data, namely medical ones. A scoping review was carried out based on a search by the Web of Science and Pubmed search engines. Four papers and two documents from the grey literature were selected. As found, the process of identifying and signaling child abuse by the health professional, and the necessary early intervention in defense of the minor as a victim of abuse, comply with the guidelines expressed in the GDPR and LGPD. This way, the notification in maltreatment scenarios by health professionals should be a priority and there shouldn’t be the fear or anxiety of legal repercussions that stands in the way of collecting and treating the data necessary for the signaling procedure that safeguards and promotes the welfare of children living with abuse.

Keywords: child abuse, disease notifications, ethics, healthcare assistance

Procedia PDF Downloads 90

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

Procedia PDF Downloads 118

Authors: Van Truong Pham

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Background: Self-management was described as a potential strategy for blood pressure control in patients with hypertension. However, the effects of self-management interventions on blood pressure, self-efficacy, medication adherence, and body mass index (BMI) in older adults with hypertension have not been systematically evaluated. We evaluated the effects of self-management interventions on systolic blood pressure (SBP) and diastolic blood pressure (DBP), self-efficacy, medication adherence, and BMI in hypertensive older adults. Methods: We followed the recommended guidelines of preferred reporting items for systematic reviews and meta-analyses. Searches in electronic databases including CINAHL, Cochrane Library, Embase, Ovid-Medline, PubMed, Scopus, Web of Science, and other sources were performed to include all relevant studies up to April 2019. Studies selection, data extraction, and quality assessment were performed by two reviewers independently. We summarized intervention effects as Hedges' g values and 95% confidence intervals (CI) using a random-effects model. Data were analyzed using Comprehensive Meta-Analysis software 2.0. Results: Twelve randomized controlled trials met our inclusion criteria. The results revealed that self-management interventions significantly improved blood pressure control, self-efficacy, medication adherence, whereas the effect of self-management on BMI was not significant in older adult patients with hypertension. The following Hedges' g (effect size) values were obtained: SBP, -0.34 (95% CI, -0.51 to -0.17, p < 0.001); DBP, -0.18 (95% CI, -0.30 to -0.05, p < 0.001); self-efficacy, 0.93 (95%CI, 0.50 to 1.36, p < 0.001); medication adherence, 1.72 (95%CI, 0.44 to 3.00, p=0.008); and BMI, -0.57 (95%CI, -1.62 to 0.48, p = 0.286). Conclusions: Self-management interventions significantly improved blood pressure control, self-efficacy, and medication adherence. However, the effects of self-management on obesity control were not supported by the evidence. Healthcare providers should implement self-management interventions to strengthen patients' role in managing their health care.

Keywords: self-management, meta-analysis, blood pressure control, self-efficacy, medication adherence, body mass index

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Authors: Tefera Kebede Leyu

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The purpose of this study was to estimate the impact of LIVES project participation on the level of technical efficiency of farm households in three regions of Ethiopia. We used household-level data gathered by IRLI between February and April 2014 for the year 2013(retroactive). Data on 1,905 (754 intervention and 1, 151 control groups) sample households were analyzed using STATA software package version 14. Efforts were made to combine stochastic frontier modeling with impact evaluation methodology using the Heckman (1979) two-stage model to deal with possible selectivity bias arising from unobservable characteristics in the stochastic frontier model. Results indicate that farmers in the two groups are not efficient and operate below their potential frontiers i.e., there is a potential to increase crop productivity through efficiency improvements in both groups. In addition, the empirical results revealed selection bias in both groups of farmers confirming the justification for the use of selection bias corrected stochastic frontier model. It was also found that intervention farmers achieved higher technical efficiency scores than the control group of farmers. Furthermore, the selectivity bias-corrected model showed a different technical efficiency score for the intervention farmers while it more or less remained the same for that of control group farmers. However, the control group of farmers shows a higher dispersion as measured by the coefficient of variation compared to the intervention counterparts. Among the explanatory variables, the study found that farmer’s age (proxy to farm experience), land certification, frequency of visit to improved seed center, farmer’s education and row planting are important contributing factors for participation decisions and hence technical efficiency of farmers in the study areas. We recommend that policies targeting the design of development intervention programs in the agricultural sector focus more on providing farmers with on-farm visits by extension workers, provision of credit services, establishment of farmers’ training centers and adoption of modern farm technologies. Finally, we recommend further research to deal with this kind of methodological framework using a panel data set to test whether technical efficiency starts to increase or decrease with the length of time that farmers participate in development programs.

Keywords: impact evaluation, efficiency analysis and selection bias, stochastic frontier model, Heckman-two step

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Authors: Iacopo Testi, Diego Pajarito, Nicoletta Roberto, Carmen Greco

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Today, a consistent segment of the world’s population lives in urban areas, and this proportion will vastly increase in the next decades. Therefore, understanding the key trends in urbanization, likely to unfold over the coming years, is crucial to the implementation of sustainable urban strategies. In parallel, the daily amount of digital data produced will be expanding at an exponential rate during the following years. The analysis of various types of data sets and its derived applications have incredible potential across different crucial sectors such as healthcare, housing, transportation, energy, and education. Nevertheless, in city development, architects and urban planners appear to rely mostly on traditional and analogical techniques of data collection. This paper investigates the prospective of the data science field, appearing to be a formidable resource to assist city managers in identifying strategies to enhance the social, economic, and environmental sustainability of our urban areas. The collection of different new layers of information would definitely enhance planners' capabilities to comprehend more in-depth urban phenomena such as gentrification, land use definition, mobility, or critical infrastructural issues. Specifically, the research results correlate economic, commercial, demographic, and housing data with the purpose of defining the youth economic discomfort index. The statistical composite index provides insights regarding the economic disadvantage of citizens aged between 18 years and 29 years, and results clearly display that central urban zones and more disadvantaged than peripheral ones. The experimental set up selected the city of Rome as the testing ground of the whole investigation. The methodology aims at applying statistical and spatial analysis to construct a composite index supporting informed data-driven decisions for urban planning.

Keywords: data science, spatial analysis, composite index, Rome, urban planning, youth economic discomfort index

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Authors: Eric Lee G. Escobedo-Wu, Nidhi Rohatgi, Fouzel Dhebar

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It is challenging for patients to navigate through healthcare systems after-hours. This leads to delays in care, patient/provider dissatisfaction, inappropriate resource utilization, readmissions, and higher costs. It is important to provide patients and providers with effective clinical decision-making tools to allow seamless connectivity and coordinated care. In August 2015, patient-centric Stanford Health Care established Clinical Advice Services (CAS) to provide clinical decision support after-hours. CAS is founded on key Lean principles: Value stream mapping, empathy mapping, waste walk, takt time calculations, standard work, plan-do-check-act cycles, and active daily management. At CAS, Clinical Assistants take the initial call and manage all non-clinical calls (e.g., appointments, directions, general information). If the patient has a clinical symptom, the CAS nurses take the call and utilize standardized clinical algorithms to triage the patient to home, clinic, urgent care, emergency department, or 911. Nurses may also contact the on-call physician based on the clinical algorithm for further direction and consultation. Since August 2015, CAS has managed 228,990 calls from 26 clinical specialties. Reporting is built into the electronic health record for analysis and data collection. 65.3% of the after-hours calls are clinically related. Average clinical algorithm adherence rate has been 92%. An average of 9% of calls was escalated by CAS nurses to the physician on call. An average of 5% of patients was triaged to the Emergency Department by CAS. Key learnings indicate that a seamless connectivity vision, cascading, multidisciplinary ownership of the problem, and synergistic enterprise improvements have contributed to this success while striving for continuous improvement.

Keywords: after hours phone calls, clinical advice services, nurse triage, Stanford Health Care

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Authors: Jared Cheves, Amanda DeChent, Joyce Pan

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Total knee replacements (TKAs) are one of the most common but painful surgical procedures performed in the United States. Currently, the gold standard for postoperative pain management is the utilization of opioids. However, in the wake of the opioid epidemic, the healthcare system is attempting to reduce opioid consumption by trialing innovative opioid sparing analgesic techniques such as continuous peripheral nerve blocks (CPNB) and continuous periarticular infiltration (CPAI). The alleviation of pain, particularly during the first 72 hours postoperatively, is of utmost importance due to its association with delayed recovery, impaired rehabilitation, immunosuppression, the development of chronic pain, the development of rebound pain, and decreased patient satisfaction. While both CPNB and CPAI are being used today, there is limited evidence comparing the two to the current standard of care or to each other. An extensive literature review was performed to explore the safety profiles and effectiveness of CPNB and CPAI in reducing reported pain scores and decreasing opioid consumption. The literature revealed the usage of CPNB contributed to lower pain scores and decreased opioid use when compared to opioid-only control groups. Additionally, CPAI did not improve pain scores or decrease opioid consumption when combined with a multimodal analgesic (MMA) regimen. When comparing CPNB and CPAI to each other, neither unanimously lowered pain scores to a greater degree, but the literature indicates that CPNB decreased opioid consumption more than CPAI. More research is needed to further cement the efficacy of CPNB and CPAI as standard components of MMA in TKA procedures. In addition, future research can also focus on novel catheter-free applications to reduce the complications of continuous catheter analgesics.

Keywords: total knee arthroplasty, continuous peripheral nerve blocks, continuous periarticular infiltration, opioid, multimodal analgesia

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Authors: John Ahluwalia

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As an affluent first-world nation, Canada took swift and comprehensive action during the outbreak of the SARS-CoV-2 (COVID-19) pandemic compared to other countries in the same socio-economic cohort. The United States has stumbled to overcome obstacles most developed nations have faced, which has led to significantly more per capita cases and deaths. The initial outbreaks of COVID-19 occurred in the US and Canada within days of each other and posed similar potentially catastrophic threats to public health, the economy, and governmental stability. On a macro level, events that take place in the US have a direct impact on Canada. For example, both countries tend to enter and exit economic recessions at approximately the same time, they are each other’s largest trading partners, and their currencies are inexorably linked. Why is it that Canada has not shared the same fate as the US (and many other nations) that have realized much worse outcomes relative to the COVID-19 pandemic? Variables intrinsic to Canada’s national infrastructure have been instrumental in the country’s efforts to flatten the curve of COVID-19 cases and deaths. Canada’s coordinated multi-level governmental effort has allowed it to create and enforce policies related to COVID-19 at both the national and provincial levels. Canada’s policy of universal healthcare is another variable. Health care and public health measures are enforced on a provincial level, and it is within each province’s jurisdiction to dictate standards for public safety based on scientific evidence. Rather than introducing confusion and the possibility of competition for resources such as PPE and vaccines, Canada’s multi-level chain of government authority has provided consistent policies supporting national public health and local delivery of medical care. This paper will demonstrate that the coordinated efforts on provincial and federal levels have been the linchpin in Canada’s relative success in containing the deadly spread of the COVID-19 virus.

Keywords: COVID-19, Canada, GIS, temporal analysis, ESRI

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Authors: G. Natuhwera, M. Rabwoni, P. Ellis, A. Merriman

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Aims: Health workers are likely to document patients’ care inaccurately, especially when using new and revised case tools, and this could negatively impact patient care. This study set out to; (1) assess nurses’ and clinicians’ documentation practices when using a new patients’ continuation case sheet (PCCS) and (2) explore nurses’ and clinicians’ experiences regarding documentation of patients’ information in the new PCCS. The purpose of introducing the PCCS was to improve continuity of care for patients attending clinics at which they were unlikely to see the same clinician or nurse consistently. Methods: This was a mixed methods study. The cross-sectional inquiry retrospectively reviewed 100 case notes of active patients on hospice and palliative care program. Data was collected using a structured questionnaire with constructs formulated from the new PCCS under study. The qualitative element was face-to-face audio-recorded, open-ended interviews with a purposive sample of one palliative care clinician, and four palliative care nurse specialists. Thematic analysis was used. Results: Missing patients’ biogeographic information was prevalent at 5-10%. Spiritual and psychosocial issues were not documented in 42.6%, and vital signs in 49.2%. Poorest documentation practices were observed in past medical history part of the PCCS at 40-63%. Four themes emerged from interviews with clinicians and nurses-; (1) what remains unclear and challenges, (2) comparing the past with the present, (3) experiential thoughts, and (4) transition and adapting to change. Conclusions: The PCCS seems to be a comprehensive and simple tool to be used to document patients’ information at subsequent visits. The comprehensiveness and utility of the PCCS does paper to be limited by the failure to train staff in its use prior to introducing. The authors find the PCCS comprehensive and suitable to capture patients’ information and recommend it can be adopted and used in other palliative and hospice care settings, if suitable introductory training accompanies its introduction. Otherwise, the reliability and validity of patients’ information collected by this PCCS can be significantly reduced if some sections therein are unclear to the clinicians/nurses. The study identified clinicians- and nurses-related pitfalls in documentation of patients’ care. Clinicians and nurses need to prioritize accurate and complete documentation of patient care in the PCCS for quality care provision. This study should be extended to other sites using similar tools to ensure representative and generalizable findings.

Keywords: documentation, information case sheet, palliative care, quality improvement

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Authors: Shamir Balderman Orit, Shamir Michal

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Israel often deals with crisis situations, but most have been characterized as security crises (e.g., war). This is the first time that the Israel has dealt with a health and social emergency as part of a global crisis. The crisis began in January 2020 with the emergence of the novel coronavirus (Covid-19), which was defined as a pandemic (World Health Organization, 2020) and arrived in Israel in early March 2020. This study examined how single mothers by choice (SMBC) experience state anxiety (SA), social support, work–family conflict (WFC), and happiness. This group has not been studied in the context of crises in general or a global crisis. Using a snowball sample, 386 SMBCanswered an online questionnaire. The findings show a negative relationship between income and level of state anxiety. State anxiety was also negatively associated with social support, level of happiness, and WFC. Finally, a stepwise regression analysis indicated that happiness explained 34% of the variance in SA. We also found that most of the women did not turn to formal support agencies such as social workers, other Government Ministries, or municipal welfare. A positive and strong correlations was also found between SA and WFC. The findings of the study reinforce the understanding that although these women made a conscious and informed decision regarding the choice of their family cell, their situation is more complex in the absence of a spouse support. Therefore, this study, as other future studies in the field of SMBC, may contribute to the improvement of their social status and the understanding that they are a unique group. Although SMBC are a growing sector of society in the past few years, there are still special needs and special attention that is needed from the formal and informal supports systems. A comparative study of these two groups and in different countries would shed light on SA among mothers in general, regardless of their relationship status and location. Researchers should expand this study by comparing mothers in relationships and exploring how SMBC coped in other countries. In summary, the findings of the study contribute knowledge on three levels: (a) knowledge about SMBC in general and during crisis situations; (b) examination of social support using tools assessing receipt of assistance and support, some of which were developed for the present study; and (c) insights regarding counseling, accompaniment, and guidance of welfare mechanisms.

Keywords: single mothers by choice, state anxiety, social support, happiness, work-family conflict

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Authors: Elissar Mansour, Emily Chen, Tracee Fernandez, Mariam Basheti, Christopher Gordon, Bandana Saini

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Sleep is an essential process for the maintenance of several neurobiological processes such as memory consolidation, mood, and metabolic processes. It is known that sleep patterns vary with age and is affected by multiple factors. While non-pharmacological strategies are generally considered first-line, sedatives are excessively used in the older population. This study aimed to explore the management of sleep in residential aged care facilities (RACFs) by nurse professionals and to identify the key factors that impact provision of optimal sleep health care. An inductive thematic qualitative research method was employed to analyse the data collected from semi-structured interviews with registered nurses working in RACF. Seventeen interviews were conducted, and the data yielded three themes: 1) the nurses’ observations and knowledge of sleep health, 2) the strategies employed in RACF for the management of sleep disturbances, 3) the organizational barriers to evidence-based sleep health management. Nurse participants reported the use of both non-pharmacological and pharmacological interventions. Sedatives were commonly prescribed due to their fast action and accessibility despite the guidelines indicating their use in later stages. Although benzodiazepines are known for their many side effects, such as drowsiness and oversedation, temazepam was the most commonly administered drug. Sleep in RACF was affected by several factors such as aging and comorbidities (e.g., dementia, pain, anxiety). However, the were also many modifiable factors that negatively impacted sleep management in RACF. These include staffing ratios, nursing duties, medication side effects, and lack of training and involvement of allied health professionals. This study highlighted the importance of involving a multidisciplinary team and the urge to develop guidelines and training programs for healthcare professionals to improve sleep health management in RACF.

Keywords: registered nurses, residential aged care facilities, sedative use, sleep

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Authors: Irmina Miernicka

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Nowadays, dress codes and widely understood appearance are becoming more important in the workplace. They are often used in the workplace to standardize image of an employer, to communicate a corporate image and ensure that customers can easily identify it. It is also a way to build professionalism of employer. Additionally, in many cases, an employer will introduce a dress code for health and safety reasons. Employers more often oblige employees to follow certain rules concerning their clothing, grooming, make-up, body art or even weight. An important research problem is to find the limits of the employer's interference with the external appearance of employees. They are primarily determined by the two main obligations of the employer, i. e. the obligation to respect the employee's personal rights and the principle of equal treatment and non-discrimination in employment. It should also be remembered that the limits of the employer's interference will be different when certain rules concerning the employee's appearance result directly from the provisions of laws and other acts of universally binding law (workwear, official clothing, and uniform). The analysis of this issue was based on literature and jurisprudence, both domestic and foreign, including the U.S. and European case law, and led the author to put forward a thesis that there are four main principles, which will protect the employer from the allegation of discrimination. First, it is the principle of adequacy - the means requirements regarding dress code must be appropriate to the position and type of work performed by the employee. Secondly, in accordance with the purpose limitation principle, an employer may introduce certain requirements regarding the appearance of employees if there is a legitimate, objective justification for this (such as work safety or type of work performed), not dictated by the employer's subjective feelings and preferences. Thirdly, these requirements must not place an excessive burden on workers and be disproportionate in relation to the employer's objective (principle of proportionality). Fourthly, the employer should also ensure that the requirements imposed in the workplace are equally burdensome and enforceable from all groups of employees. Otherwise, it may expose itself to grounds of discrimination based on sex or age. At the same time, it is also possible to differentiate the situation of some employees if these differences are small and reflect established habits and traditions and if employees are obliged to maintain the same level of professionalism in their positions. Although this subject may seem to be insignificant, frequent application of dress codes and increasing awareness of both employees and employers indicate that its legal aspects need to be thoroughly analyzed. Many legal cases brought before U.S. and European courts show that employees look for legal protection when they consider that their rights are violated by dress code introduced in a workplace.

Keywords: labor law, the appearance of an employee, discrimination in the workplace, dress code in a workplace

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Authors: Nelson Kimeli Kemboi Yego, Juma Kasozi, Joseph Nkruzinza, Francis Kipkogei

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The study investigated the low uptake of health insurance in Kenya despite efforts to achieve universal health coverage through various health insurance schemes. Unsupervised machine learning techniques were employed to identify patterns in health insurance uptake based on sociodemographic factors among Kenyan households. The aim was to identify key demographic groups that are underinsured and to provide insights for the development of effective policies and outreach programs. Using the 2021 FinAccess Survey, the study clustered Kenyan households based on their health insurance uptake and sociodemographic features to reveal patterns in health insurance uptake across the country. The effectiveness of k-prototypes clustering, hierarchical clustering, and agglomerative hierarchical clustering in clustering based on sociodemographic factors was compared. The k-prototypes approach was found to be the most effective at uncovering distinct and well-separated clusters in the Kenyan sociodemographic data related to health insurance uptake based on silhouette, Calinski-Harabasz, Davies-Bouldin, and Rand indices. Hence, it was utilized in uncovering the patterns in uptake. The results of the analysis indicate that inclusivity in health insurance is greatly related to affordability. The findings suggest that targeted policy interventions and outreach programs are necessary to increase health insurance uptake in Kenya, with the ultimate goal of achieving universal health coverage. The study provides important insights for policymakers and stakeholders in the health insurance sector to address the low uptake of health insurance and to ensure that healthcare services are accessible and affordable to all Kenyans, regardless of their socio-demographic status. The study highlights the potential of unsupervised machine learning techniques to provide insights into complex health policy issues and improve decision-making in the health sector.

Keywords: health insurance, unsupervised learning, clustering algorithms, machine learning

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Authors: Mudrika Singhal

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The research paper is basically about how the genetically modified organisms evolved and their significance in today’s world. It also highlights about the various pros and cons of the genetically modified organisms and the progress of India in this field. A genetically modified organism is the one whose genetic material has been altered using genetic engineering techniques. They have a wide range of uses such as transgenic plants, genetically modified mammals such as mouse and also in insects and aquatic life. Their use is rooted back to the time around 12,000 B.C. when humans domesticated plants and animals. At that humans used genetically modified organisms produced by the procedure of selective breeding and not by genetic engineering techniques. Selective breeding is the procedure in which selective traits are bred in plants and animals and then are domesticated. Domestication of wild plants into a suitable cultigen is a well known example of this technique. GMOs have uses in varied fields ranging from biological and medical research, production of pharmaceutical drugs to agricultural fields. The first organisms to be genetically modified were the microbes because of their simpler genetics. At present the genetically modified protein insulin is used to treat diabetes. In the case of plants transgenic plants, genetically modified crops and cisgenic plants are the examples of genetic modification. In the case of mammals, transgenic animals such as mice, rats etc. serve various purposes such as researching human diseases, improvement in animal health etc. Now coming upon the pros and cons related to the genetically modified organisms, pros include crops with higher yield, less growth time and more predictable in comparison to traditional breeding. Cons include that they are dangerous to mammals such as rats, these products contain protein which would trigger allergic reactions. In India presently, group of GMOs include GM microorganisms, transgenic crops and animals. There are varied applications in the field of healthcare and agriculture. In the nutshell, the research paper is about the progress in the field of genetic modification, taking along the effects in today’s world.

Keywords: applications, mammals, transgenic, engineering and technology

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Authors: Anggun Yuliana Putri, Melisa Rahmadini

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The Ministry of National Development Planning in Indonesia has reported that more than eight million Indonesian children are still malnourished. Based on national statistics, and a recent ranking from NGO Save the Children, Indonesia is one of 15 countries making the fastest gains in cutting child malnutrition among 165 developing countries. According to a United Nations Children’s Fund, at least 7.6 million Indonesian children under the age of 5 or one out of every three suffer from stunted growth, a primary manifestation of malnutrition in early childhood, the report ranked Indonesia as having the fifth largest number of children under 5 suffering from stunted growth worldwide. Addressing the problem of malnutrition in Indonesia, Aksi Cepat Tanggap (ACT) Foundation, a humanitarian organization working with Carrefour, acts as donor and pursues several solutions to the problem, especially of malnourished children and infants in South Tangerang area, Indonesia. The objective of this study was to examine the community empowerment driven by ACT Foundation in order to maintain the good status continuity of child and toddler after the children malnutrition recovered. Research was conducted using qualitative approach through in-depth interview and observation to find out how the Bengkel Gizi Terpadu (Integrated Nutrion Workshop) programs make people empowered. Bengkel Gizi Terpadu (BGT) is divided into 3 sequences of activities, there were: integrated malnutrition rehabilitation; provision of health education to mothers of infants and young children; and family economic empowerment to head of household. Results showed that after empowerment process has been done through training and provision of knowledge to the mothers and families about the important of nutrition and health, there were 30 of 100 mothers who participated actively. Then, there were 45 of 100 heads of household who participated in business training were able to open a business on their own which provided and controlled by ACT as stakeholder in this program. The further findings revealed that BGT programs are able to form community health workers and provide employment opportunities to community. This study believes that integrated nutrition workshop program is the solution to maintain good nutrition among children in South Tangerang, through empowerment of parents and community members, via education and business training program. Both programs prepared parents with economic sustenance and necessary information, a pre-requisite to end malnutrition in children.

Keywords: community, empowerment, malnutrition, training

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Authors: Gustavo Cruz-Bello

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Progreso is a coastal community in Yucatan, Mexico, highly exposed to floods produced by severe storms and tropical cyclones. A participatory cartography approach was conducted to help to reduce floods disasters and assess social vulnerability within the community. The first step was to engage local authorities in risk management to facilitate the process. Two workshop were conducted, in the first, a poster size printed high spatial resolution satellite image of the town was used to gather information from the participants: eight women and seven men, among them construction workers, students, government employees and fishermen, their ages ranged between 23 and 58 years old. For the first task, participants were asked to locate emblematic places and place them in the image to familiarize with it. Then, they were asked to locate areas that get flooded, the buildings that they use as refuges, and to list actions that they usually take to reduce vulnerability, as well as to collectively come up with others that might reduce disasters. The spatial information generated at the workshops was digitized and integrated into a GIS environment. A printed version of the map was reviewed by local risk management experts, who validated feasibility of proposed actions. For the second workshop, we retrieved the information back to the community for feedback. Additionally a survey was applied in one household per block in the community to obtain socioeconomic, prevention and adaptation data. The information generated from the workshops was contrasted, through T and Chi Squared tests, with the survey data in order to probe the hypothesis that poorer or less educated people, are less prepared to face floods (more vulnerable) and live near or among higher presence of floods. Results showed that a great majority of people in the community are aware of the hazard and are prepared to face it. However, there was not a consistent relationship between regularly flooded areas with people’s average years of education, house services, or house modifications against heavy rains to be prepared to hazards. We could say that the participatory cartography intervention made participants aware of their vulnerability and made them collectively reflect about actions that can reduce disasters produced by floods. They also considered that the final map could be used as a communication and negotiation instrument with NGO and government authorities. It was not found that poorer and less educated people are located in areas with higher presence of floods.

Keywords: climate change, floods, Mexico, participatory mapping, social vulnerability

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Authors: F. Fatoye, C. E. Mbada, T. Gebrye, A. O. Ogunsola, C. Fatoye, O. Oyewole

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Musculoskeletal disorders (MSDs) such as low back pain (LBP), cervical spondylosis (CSPD), sprain, osteoarthritis (OA), and post immobilization stiffness (PIS) have a major impact on individuals, health systems and society in terms of morbidity, long-term disability, and economics. This study estimated the direct and indirect costs of common MSDs in Osun State, Nigeria. A review of medical charts for adult patients attending Physiotherapy Outpatient Clinic at the Obafemi Awolowo University Teaching Hospitals Complex, Osun State, Nigeria between 2009 and 2018 was carried out. The occupational class of the patients was determined using the International Labour Classification (ILO). The direct and indirect costs were estimated using a cost-of-illness approach. Physiotherapy related health resource use, and costs of the common MSDs, including consultation fee, total fee charge per session, costs of consumables were estimated. Data were summarised using descriptive statistics mean and standard deviation (SD). Overall, 1582 (Male = 47.5%, Female = 52.5%) patients with MSDs population with a mean age of 47.8 ± 25.7 years participated in this study. The mean (SD) direct costs estimate for LBP, CSPD, PIS, sprain, OA, and other conditions were $18.35 ($17.33), $34.76 ($17.33), $32.13 ($28.37), $35.14 ($44.16), $37.19 ($41.68), and $15.74 ($13.96), respectively. The mean (SD) indirect costs estimate of LBP, CSPD, PIS, sprain, OA, and other MSD conditions were $73.42 ($43.54), $140.57 ($69.31), $128.52 ($113.46), sprain $140.57 ($69.31), $148.77 ($166.71), and $62.98 ($55.84), respectively. Musculoskeletal disorders contribute a substantial economic burden to individuals with the condition and society. The unacceptable economic loss of MSDs should be reduced using appropriate strategies. Further research is required to determine the clinical and cost effectiveness of strategies to improve health outcomes of patients with MSDs. The findings of the present study may assist health policy and decision makers to understand the economic burden of MSDs and facilitate efficient allocation of healthcare resources to alleviate the burden associated with these conditions in Nigeria.

Keywords: economic burden, low back pain, musculoskeletal disorders, real-world

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Authors: Kushal Patel, Manasa Dittakavi, Cyrus Falsafi, Gretchen Lovett

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Rural America has seen a surge in opioid addiction rates and overdose deaths in recent years, becoming a significant public health crisis. This may be due to a variety of factors, such as lack of access to healthcare or other economic and social factors that can contribute to addiction such as poverty, unemployment, and social isolation. As the opioid epidemic has disproportionately affected rural communities, pregnant women in these areas may be highly susceptible and face additional difficulties in facing the appropriate care they need. Opioid use disorder has many negative effects on prenatal infants. These include changes in their microbiome, mental health, neurodevelopment and cognition. These can affect how the child performs in various activities in life and how they interact with others. It has been demonstrated that using cognitive behavioral therapy improves not just pain-related results but also mobility, quality of life, disability, and mood outcomes. This indicates that cognitive behavioral therapy (CBT) may be a useful therapeutic strategy for enhancing general health and wellbeing in people with opioid use problems. In terms of treating psychiatric diseases, CBT carries fewer dangers than opioids. One study that illustrates the potential for CBT to promote a reduction in opioid use disorder used self-reported drug use patterns 6 months prior to and during their pregnancy. At the beginning of the study, participants reported an average of 3.78 drug or alcohol use days in the previous 28 days, which decreased to 1.63 days after treatment. The study also found a decrease in depression scores, as measured by IDS scores, from 23.9 to 17.1 at the end of treatment. These and other results show that CBT can have meaningful impacts on pregnant women in Rural America who struggle with an opioid use disorder. This project has been approved by the West Virginia School of Osteopathic Medicine- Office of Research and Sponsored Programs and deemed non-research scholarly work.

Keywords: appalachia, CBT, opiods, pregnancy

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Authors: Charléa M. Smith, Raiden L. Schodowski, Arletty Pinel

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Due to the COVID-19 pandemic, the Faculty of Medical Sciences of The University of the West Indies (UWI) Mona in Kingston, Jamaica, had to rapidly migrate to digital and blended learning. Students in the preclinical stage of the program transitioned to full-time online learning, while students in the clinical stage experienced decreased daily patient contact and the implementation of a blend of online lectures and virtual clinical practice. Such sudden changes were coupled with the institutional pressure of the need to introduce a novel approach to education without much time for preparation, as well as additional strain endured by the faculty, who were overwhelmed by serving as frontline workers. During the period July 20 to August 23, 2021, this study surveyed preclinical and clinical students to capture their experiences with these changes and their recommendations for future use of digital modalities of learning to enhance medical education. It was conducted with a fellow student of the 2021 cohort of the MultiPod mentoring program. A questionnaire was developed and distributed digitally via WhatsApp to all medical students of the UWI Mona campus to assess students’ experiences and perceptions of the advantages, challenges, and impact on individual knowledge proficiencies brought about by the transition to predominantly digital learning environments. 108 students replied, 53.7% preclinical and 46.3% clinical. 67.6% of the total were female and 30.6 % were male; 1.8% did not identify themselves by gender. 67.2% of preclinical students preferred blended learning and 60.3% considered that the content presented did not prepare them for clinical work. Only 31% considered that the online classes were interactive and encouraged student participation. 84.5% missed socialization with classmates and friends and 79.3% missed a focused environment for learning. 80% of the clinical students felt that they had not learned all that they expected and only 34% had virtual interaction with patients, mostly by telephone and video calls. Observing direct consultations was considered the most useful, yet this was the least-used modality. 96% of the preclinical students and 100% of the clinical ones supplemented their learning with additional online tools. The main recommendations from the survey are the use of interactive teaching strategies, more discussion time with lecturers, and increased virtual interactions with patients. Universities are returning to face-to-face learning, yet it is unlikely that blended education will disappear. This study demonstrates that students’ perceptions of their experience during mobility restrictions must be taken into consideration in creating more effective, inclusive, and efficient blended learning opportunities.

Keywords: blended learning, digital learning, medical education, student perceptions

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Authors: Anne Hovgaard Jorgensen

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This study investigates how Muslim fathers in Denmark are struggling to overcome notions of mistrust from teachers and educators. Starting from school-home-cooperation (parent conferences, school-home communication, etc.), the study finds that many Muslim fathers do not feel acknowledged as a resource in the upbringing of their children. To explain these experiences further, the study suggest the notion of ‘mistrusted masculinity’ to grasp the controlling image these fathers meet in various schools and child-care-institutions in the Danish Welfare state. The paper is based on 9 months of fieldwork in a Danish school, a social housing area and in various ‘father groups’ in Denmark. Additional, 50 interviews were conducted with fathers, children, mothers, schoolteachers, and educators. By using Connell's concepts 'hegemonic' and 'marginalized' masculinity as steppingstones, the paper argues that these concepts might entail a too static and dualistic picture of gender. By applying the concepts of 'emergent masculinity' and 'emergent fatherhood' the paper brings along a long needed discussion of how Muslim men in Denmark are struggling to overcome and change the controlling images of them as patriarchal and/or ignorant fathers regarding the upbringing of their children. As such, the paper shows how Muslim fathers are taking action to change this controlling image, e.g. through various ‘father groups’. The paper is inspired by the phenomenological notions of ‘experience´ and in the light of this notion, the paper tells the fathers’ stories about their upbringing of their children and aspirations for fatherhood. These stories share light on how these fathers take care of their children in everyday life. The study also shows that the controlling image of these fathers have affected how some Muslim fathers are actually being fathers. The study shows that fear of family-interventions from teachers or social workers e.g. have left some Muslim fathers in a limbo, being afraid of scolding their children, and being confused of ‘what good parenting in Denmark is’. This seems to have led to a more lassie fair upbringing than these fathers actually wanted. This study is important since anthropologists generally have underexposed the notion of fatherhood, and how fathers engage in the upbringing of their children. Over more, the vast majority of qualitative studies of fatherhood have been on white middleclass fathers, living in nuclear families. In addition, this study is crucial at this very moment due to the major refugee crisis in Denmark and in the Western world in general. A crisis, which has resulted in a vast number of scare campaigns against Islam from different nationalistic political parties, which enforces the negative controlling image of Muslim fathers.

Keywords: fatherhood, Muslim fathers, mistrust, education

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Authors: Lukas Kober, Vladimir Littva, Vladimir Siska

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The pandemic has had a significant impact on our lives. One of the most affected professions is the nursing profession. Nurses are closest to the patient, spend the most time with him, support him, often replace the closest family members, and of course, are part of the whole treatment process. Current nurses have more competencies and roles than in the past. The healthcare system has reached a turning point, also in connection with the spreading Delta variant and the risk of the arrival of the third wave. The lack of nurses is a long-term problem, but it did not arise by itself. The reasons for the departure of nurses from the health care system are not only due to the increasing average age of nurses and midwives in Slovakia and their retirement. Thousands of nurses are leaving due to poor working conditions, low wages, and poor management of individual workplaces. We need to keep older nurses in the health care system, otherwise, we risk their early departure. The pandemic only exacerbates this situation, and the associated risks, such as occupational infections or enormous overload and exhaustion, only accelerate the exit from the profession. According to current data from the register of nurses and midwives, we canceled 772 registrations from January to September 2021, and 584 nurses requested the suspension of registration due to non-performance of the profession. During the same period, we registered only 240 new nurses graduate. We have had this significant disparity here for a long time. For the whole of 2020, we canceled 911 registrations and suspended 973 registrations. We registered a total of 389 graduates. Our system loses hundreds of graduates a year and loses experienced nurses with decades of experience who leave due to poor working conditions, wages and suffer from burnout. Such compensation should also be awarded to the families of health professionals who have lost their lives due to work and to COVID-19. These options can also be motivating for promising people interested in studying nursing, who can gradually replace the missing workforce. This purchase is supported by the KEGA project no. 015KU-4/2019.

Keywords: pandemic, COVID-19, nursing, nursing workforce, lack of nurses

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Authors: Ibrahim Khan, Waqas Khalid

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The Healthcare sector is known to consume a higher proportion of total energy consumption in the HVAC market owing to an excessive cooling and heating requirement in maintaining human thermal comfort in indoor conditions, catering to patients undergoing treatment in hospital wards, rooms, and intensive care units. The indoor thermal comfort conditions in selected hospitals of Islamabad, Pakistan, were measured on a real-time basis with the collection of first-hand experimental data using calibrated sensors measuring Ambient Temperature, Wet Bulb Globe Temperature, Relative Humidity, Air Velocity, Light Intensity and CO2 levels. The Experimental data recorded was analyzed in conjunction with the Thermal Comfort Questionnaire Surveys, where the participants, including patients, doctors, nurses, and hospital staff, were assessed based on their thermal sensation, acceptability, preference, and comfort responses. The Recorded Dataset, including experimental and survey-based responses, was further analyzed in the development of a correlation between operative temperature, operative relative humidity, and other measured operative parameters with the predicted mean vote and adaptive predicted mean vote, with the adaptive temperature and adaptive relative humidity estimated using the seasonal data set gathered for both summer – hot and dry, and hot and humid as well as winter – cold and dry, and cold and humid climate conditions. The Machine Learning Logistic Regression Algorithm was incorporated to train the operative experimental data parameters and develop a correlation between patient sensations and the thermal environmental parameters for which a new ML-based adaptive thermal comfort model was proposed and developed in our study. Finally, the accuracy of our model was determined using the K-fold cross-validation.

Keywords: predicted mean vote, thermal comfort, energy management, logistic regression, machine learning

Procedia PDF Downloads 57