Respiratory Therapy Students’ Perceptions of Effective Teaching Characteristics of Clinical Instructors at an Urban University

Clinical instructors may have a negative or positive effect on student’s clinical practice. The behavioral characteristics of respiratory therapy clinical instructors are vital to the success of student’s clinical learning experience. Therefore, respiratory therapy student’s perception of the effectiveness of the clinical instructor’s behavior is an important indicator to modify and facilitate effective clinical instruction. PURPOSE: The purpose of this study was to identify the most effective clinical teaching behaviors (ECTB) perceived by undergraduate respiratory therapy (BSRT) and integrated graduate respiratory therapy (MSRT) students and to identify any similarities in their rankings. METHODS: The study used descriptive exploratory design with a self-reporting survey. The survey was administered to a convenience sample of first and second year BSRT and MSRT students attending an accredited respiratory therapy program at an urban university located in the southeastern United States. The survey consisted of 35 teaching behaviors presented on a five-point Likert scale according to importance. The collected data were analyzed using descriptive statistics. RESULTS: Seventy-two students were surveyed, more than two-thirds of the respondents were female. Seventy-five percent of respondents studied were BSRT, which females accounted for 78% and males 22%. Graduate MSRT studied were 25% of the total sample with females and males equally split at 50%. Two thirds of MSRT students reported previous education with BSRT students reporting less than one-quarter. The study findings indicate BSRT and MSRT students’ perceptions ranking of the most important behavioral characteristics hold similarities but both perceive the ordered rank of importance differently. Both BSRT and MSRT students ranked “be approachable” as the most important clinical behavioral characteristic with mean scores and S.D respectively (M 4.89, S.D ±0.37, and M 4.94, S.D ±0.24). Additionally, BSRT students rank the characteristic “respect student as an individual” (M 4.87, S.D ±0.34) next significant while MSRT students rank “demonstrate self-control & patience” (M 4.94, S.D ±0.23) the next highest. CONCLUSION: Although BSRT and MSRT students’ perceptions demonstrated similarities, mean scores data between first year and second year show a shift in ranking between characteristics. This may be because student’s perceptions could change as they advance in their clinical course work or their past educational experience. In addition, the results may assist respiratory therapy clinical instructors to appreciate students’ views and acknowledge areas of success as well as areas needing improvement

Dietary nitrate supplementation and cardiovascular risk outcomes in chronic obstructive pulmonary disease

It has been shown that acute/short-term dietary nitrate supplementation improves cardiovascular (CV) risk outcomes. However, it is not clear if any long-term beneficial effect is present. So, the overall aim of this thesis was to investigate the effect of prolonged consumption of daily nitrate rich beetroot juice (NR-BRJ) on CV risks in patients with COPD over three months. A pooled result from the meta-analysis I conducted at the start of my PhD has shown a reduction in BP due to short term consumption of nitrate supplement. However, the longest and largest study combined the intervention with pulmonary rehabilitation leading to inconclusive results. Therefore, I undertook a 3-month randomised controlled trial (the ON-BC study) recruiting COPD patients with systolic blood pressure (SBP) ≥130 mmHg. We observed for the first time a sustained reduction in SBP in the (NR-BRJ) intervention group as compared to placebo (Pl-BRJ) median (IQR): Δ -4 mm Hg, (-6, -2), vs. Δ -1 mmHg (-2, 1); an estimated treatment effect -3 mm Hg, (-6, 1), P<0.001), respectively. This was associated with a clinically significant improvement in exercise capacity (6-minute walk distance). To explore the potential mechanisms associated with the observed change in SBP, I studied several vascular markers. Novel findings in this section include a fall in plasma asymmetric dimethylarginine (ADMA), and an increase in the plasma L-arginine/ADMA ratio. Endothelial function assessed using peripheral arterial tonometry (PAT) improved following NR-BRJ supplement, but there was no change in platelet aggregation. In summary, this thesis has shown that dietary nitrate supplementation in the form of NR-BRJ exerts a sustained (12 weeks) effect on blood pressure in people with COPD, accompanied by improvements in other markers of endothelial function and vascular risk. Further longer-term trials are needed to examine the effects of BRJ at CVD event rates and establish whether the effect on exercise capacity translates into a meaningful improvement in daily life physical activity.Open Acces

Effect Of Servant Leadership On Employees’ Empowerment, Knowledge Sharing And Creativity In Al-Masjid Al-Haram And Al-Masjid Al-Nabawi

Every year, The “General Presidency of Al-Masjid Al-Haram and Al-Masjid Al-Nabawi” organization in Saudi Arabia serves millions of visitors to perform Umrah and Hajj. With reference to this organization, its employees need to be more creative and demonstrate a good relationship with their leaders to provide quality and efficient services and ensure high satisfaction levels among local and international Hajj and Umrah pilgrims. This study aims to examine the relevance of servant leadership in the organization and how the different dimensions of servant leadership enhance the employees’ creativity. In addition, the study investigates the mediating effect of employees’ empowerment on the relationship between servant leadership dimensions and employees’ creativity and, the moderating role of knowledge sharing on the relationship between employees’ empowerment and employees’ creativity

English Language Teaching

Abstract The countries that use English as a foreign language need effective activities which propel students to practice skills of the language properly inside as well as outside classrooms. Debating is a practice that inspires learners to open their mouth, get into discussion, defend their own positions, place counter arguments and also conduct research on related issues. While debating in English, the debaters get involved into a challenging and thrilling activity; moreover, they find themselves well-conversant in the aforesaid language. This paper presents the rationale behind using debate in EFL classes and proposes a few modules of debating which, if practiced properly, will make students confident users of English language in academic, social and professional settings. The paper also examines utilities of the modules and exhibits how students while practicing debate can improve their English language as well as presentation skills. The modules can be practiced in EFL classes, English language centers, debating clubs or other formal and informal settings where teaching-learning of English language is concerned

IoT Framework for a Decision-Making System of Obesity and Overweight Extrapolation among Children, Youths, and Adults

Approximately 30% of the global population is suffering from obesity and being overweight, which is approximately 2.1 billion people worldwide. The ratio is expected to surpass 40% by 2030 if the current balance continues to grow. The global pandemic due to COVID-19 will also impact the predicted obesity rates. It will cause a significant increase in morbidity and mortality worldwide. Multiple chronic diseases are associated with obesity and several threat elements are associated with obesity. Various challenges are involved in the understanding of risk factors and the ratio of obesity. Therefore, diagnosing obesity in its initial stages might significantly increase the patient’s chances of effective treatment. The Internet of Things (IoT) has attained an evolving stage in the development of the contemporary environment of healthcare thanks to advancements in information and communication technologies. Therefore, in this paper, we thoroughly investigated machine learning techniques for making an IoT-enabled system. In the first phase, the proposed system analyzed the performances of random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), logistic regression (LR), and naïve Bayes (NB) algorithms on the obesity dataset. The second phase, on the other hand, introduced an IoT-based framework that adopts a multi-user request system by uploading the data to the cloud for the early diagnosis of obesity. The IoT framework makes the system available to anyone (and everywhere) for precise obesity categorization. This research will help the reader understand the relationships among risk factors with weight changes and their visualizations. Furthermore, it also focuses on how existing datasets can help one study the obesity nature and which classification and regression models perform well in correspondence to others

Machine-Learning-Enabled Obesity Level Prediction Through Electronic Health Records

Obesity is a critical health condition that severely affects an individual’s quality of life and well-being. The occurrence of obesity is strongly associated with extreme health conditions, such as cardiac diseases, diabetes, hypertension, and some types of cancer. Therefore, it is vital to avoid obesity and or reverse its occurrence. Incorporating healthy food habits and an active lifestyle can help to prevent obesity. In this regard, artificial intelligence (AI) can play an important role in estimating health conditions and detecting obesity and its types. This study aims to see obesity levels in adults by implementing AI-enabled machine learning on a real-life dataset. This dataset is in the form of electronic health records (EHR) containing data on several aspects of daily living, such as dietary habits, physical conditions, and lifestyle variables for various participants with different health conditions (underweight, normal, overweight, and obesity type I, II and III), expressed in terms of a variety of features or parameters, such as physical condition, food intake, lifestyle and mode of transportation. Three classifiers, i.e., eXtreme gradient boosting classifier (XGB), support vector machine (SVM), and artificial neural network (ANN), are implemented to detect the status of several conditions, including obesity types. The findings indicate that the proposed XGB-based system outperforms the existing obesity level estimation methods, achieving overall performance rates of 98.5% and 99.6% in the scenarios explored

Artificial Intelligence and Internet of Things Enabled Intelligent Framework for Active and Healthy Living

Obesity poses several challenges to healthcare and the well-being of individuals. It can be linked to several life-threatening diseases. Surgery is a viable option in some instances to reduce obesity-related risks and enable weight loss. State-of-the-art technologies have the potential for long-term benefits in post-surgery living. In this work, an Internet of Things (IoT) framework is proposed to effectively communicate the daily living data and exercise routine of surgery patients and patients with excessive weight. The proposed IoT framework aims to enable seamless communications from wearable sensors and body networks to the cloud to create an accurate profile of the patients. It also attempts to automate the data analysis and represent the facts about a patient. The IoT framework proposes a co-channel interference avoidance mechanism and the ability to communicate higher activity data with minimal impact on the bandwidth requirements of the system. The proposed IoT framework also benefits from machine learning based activity classification systems, with relatively high accuracy, which allow the communicated data to be translated into meaningful information

Machine Learning and Internet of Things Enabled Monitoring of Post-Surgery Patients: A Pilot Study

Artificial Intelligence (AI) and Internet of Things (IoT) offer immense potential to transform conventional healthcare systems. The IoT and AI enabled smart systems can play a key role in driving the future of smart healthcare. Remote monitoring of critical and non-critical patients is one such field which can leverage the benefits of IoT and machine learning techniques. While some work has been done in developing paradigms to establish effective and reliable communications, there is still great potential to utilize optimized IoT network and machine learning technique to improve the overall performance of the communication systems, thus enabling fool-proof systems. This study develops a novel IoT framework to offer ultra-reliable low latency communications to monitor post-surgery patients. The work considers both critical and non-critical patients and is balanced between these to offer optimal performance for the desired outcomes. In addition, machine learning based regression analysis of patients’ sensory data is performed to obtain highly accurate predictions of the patients’ sensory data (patients’ vitals), which enables highly accurate virtual observers to predict the data in case of communication failures. The performance analysis of the proposed IoT based vital signs monitoring system for the post-surgery patients offers reduced delay and packet loss in comparison to IEEE low latency deterministic networks. The gradient boosting regression analysis also gives a highly accurate prediction for slow as well as rapidly varying sensors for vital sign monitoring

Quality of Life of Patients with Chronic Kidney Disease

This study aimed at exploring the assessment of Quality of Life (QOL) in patients with Chronic Kidney Disease (CKD), and aiming at assessing the QOL of patients with stages 1–5 Chronic Kidney Disease CKD on conservative treatment in order to identify a possible association between Quality Of Life QOL and progression of kidney insufficiency. The results were compared with those obtained for patients on hemodialysis. Sociodemographic, clinical and laboratory data were also evaluated. And the study concluded that research efforts have expanded significantly to determine the state of pediatric CKD patient HRQOL and the factors that impact HRQOL across all stages of CKD and all modalities of renal replacement therapy. Data from all studies suggest that children with a renal transplant fare better with respect to HRQOL than those receiving dialysis

Analysis of Gabapentinoids Abuse-Reports in the Middle East and North Africa Region Utilizing the Food and Drug Administration Adverse Event Reporting System

Objectives: The purpose of this study was to identify abuse-related post-marketing reports associated with gabapentinoids use in the Middle East and North Africa (MENA) region countries. Methods: A retrospective cross-sectional analysis of abuse-related adverse drug event (ADE) reports from the Middle East and North Africa (MENA) region. It was performed using the Food and Drug Administration Adverse Event Reporting System (FAERS) database from January 2008 through June 2020. Abuserelated ADE reports for gabapentin and pregabalin were extracted from the FAERS database. Descriptive statistics were performed, and the proportional reporting ratio (PRR) was calculated to detect disproportional attribution of abuse-related ADEs for gabapentin versus pregabalin. Results: We identified 559 all-cause ADE reports for gabapentinoids, including 205 (36.7%) abuse-related ADE reports reported to FAERS in the period of analysis. FAERS included 139 (67.8%) pregabalin and 66 (32.2%) gabapentin abuse-related ADE reports. Among MENA region countries, Turkey (55, 39.6%) and Saudi Arabia (34, 23.7%) had the highest number of abuse-related ADE reports for pregabalin. The most pregabalin abuse-related ADE reports involved adult male patients. The PRR of pregabalin versus gabapentin abuse-related ADE reports was 1.11, indicating that the number of abuse-related events was higher for pregabalin compared to gabapentin. Conclusion: Over 200 cases of abuserelated gabapentinoids events were reported to FEARS from the MENA region in the study period. Further studies should assess risk factors and potential programs to reduce gabapentinoids abuse