Background: It is well accepted that gestational weight gain (GWG) outside of the Institute of Medicine recommendations is a modifiable risk factor for pregnancy complications, regardless of a woman’s pre-pregnancy body mass index. Objectives: Given that not all prenatal healthcare providers (HCPs) are aware of these guidelines nor discuss them with their patients, this thesis sought to examine counselling practices and evaluate a pilot knowledge translation tool for the improvement of such counselling. Methods: 1) A literature review was conducted to assess current prenatal GWG counselling practices between patients and HCPs; 2) an electronic survey was conducted to determine patients’ perceptions of GWG counselling and whether certain patient characteristics increase chances of counselling; 3) a quasi-experimental study was implemented to evaluate the Canadian Obesity Network’s 5As of Healthy Pregnancy Weight Gain tool. Results: GWG counselling was often infrequent and inaccurate. Women of higher socioeconomic status, older age, nulliparous, history of dieting, low physical activity and those categorized with overweight/obesity were more likely to receive GWG advice. The 5As of Healthy Pregnancy Weight Gain Tool is effective at initiating Ask and Advise components of HCP-mediated GWG counselling, but work is still needed to improve all other components of the 5As (Assess, Agree, Assist). Conclusions: Weight gain counselling is an essential component of prenatal healthcare, but discussions between HCPs and patients is inconsistent. Future steps include the development of GWG knowledge translation tools and improving GWG education for HCPs.

Due to the increase in dual-income families, work-family conflict has become a more prevalent phenomenon in today's society. Home healthcare workers have been previously identified as an employment group that is susceptible to high levels of burnout and low levels of job satisfaction, yet work-family conflict concerns have yet to be examined. Particularly because of the great deal of care being provided within a home, both at work and in life, this population is of particular interest for examining work-family conflict. The purpose of this study is to empirically investigate the relationships among work-family conflict, job satisfaction, affectivity, and burnout within a sample of rural, home healthcare employees. More specifically, four distinct models are proposed which include the following variables: positive and negative affectivity, number of hours providing care for others outside of work, number of hours worked per week, family-interference with work conflict, work-interference with family conflict, job satisfaction and three facets of burnout (personal accomplishment, depersonalization, and emotional exhaustion). While models predicting job satisfaction and emotional exhaustion accounted for the most variance, all four models provided information regarding the direct, indirect and mediating relationships of the aforementioned variables. More specifically, the findings suggest that the two types of work-family conflict uniquely mediate the proposed outcome variables highlighting the importance of examining work-family conflict from a more refined perspective. Exploratory group differences are also examined. This study contributes to a gap in the literature examining individuals' experiences of work-family conflict, job satisfaction, and burnout who are employed in a specific career field. Practical, research, and theoretical implications are discussed.

The movement of nursing professionals from the public sector to the private sector, and from the private sector to foreign countries severely impact on the ability of developing countries to meet their domestic health care needs. In South Africa, the public health care system is facing serious human resource constraints, due to this migration. There simply aren’t enough experienced nurses to manage the escalating health care service consumption caused by factors such as population growth, increased burden of disease, the HIV/AIDS pandemic and decreased training of nursing personnel. A staggering 37 801 doctor and nurse posts are vacant in public hospitals and clinics (Kahn, 2008). Unless improved human resource management strategies are implemented urgently, the migration of health care workers from especially public service health institutions in South Africa will seriously hamper implementation of the stated health care reform strategy. This study investigates the reasons why South African nurses are leaving the public and private health sector, or their profession, or even the country as a whole. Based on the results of a survey of 67 nurses in the private and public health sectors in the northern Kwazulu Natal area, the study found that unfavorable working conditions together with low levels of job satisfaction caused by perceived reasons such as insufficient salaries, limited career advancement, ineffective management, excessive workload and safety concerns led to this state of job satisfaction. Most of the drivers responsible for this exodus can be attributed to the real or perceived deterioration in socio-political factors. The recommendations for the health care sector in South Africa are; • Review nurses salaries annually – not only during restructuring or crisis situations. • Ensure that nurse’s remuneration packages are competitive with those of similar professions. • Pay nurses incentives for working unsocial hours. • Pay nurses bonuses for acquiring additional qualifications. • Pay nurses who work late shifts additional allowances. • Train nursing managers. • Provide training and education opportunities for nursing staff. • Respect should be shown by managers, physicians and colleagues. • Improve the workplace environment and working conditions of nursing staff. • Improve overall morale by rewarding excellence and treating nurses with respect and dignity.

Wireless devices have invaded the medical industry with a wide range of capability as components of a wireless personal area network (WPAN) and Wireless Body Area Network (WBAN). The recent advances in Internet of Things (IoT) promises even larger contributions to the future of medical applications. This paper investigates the Bluetooth Low Energy (Bluetooth Smart- BTLE) for indoor localization of HealthCare devices used in medical telemetry applications and demonstrates the key role that localization plays in tracking of Bluetooth Low Energy enabled medical devices. Proper tracking of these devices provides better management which would directly reduce the transmission of infectious diseases which can result from improper sharing of these devices. This work will investigate the novel indoor localization technologies of BTLE devices with creative research strategies, and their applications as a basis for ultimately improving health standard using BTLE localization.

Magister Curationis - MCur / Infections (including healthcare-associated infections) are one of the leading causes of neonatal morbidity and mortality, yet these deaths could be prevented by cost-effective interventions. Handwashing (HW) is crucial to preventing the spread of antimicrobial resistance and reducing healthcare-associated infections (HCAIs). However, healthcare workers' compliance with optimal practices remains low in most settings.The purpose of this study was to determine the compliance with HW among nurses and midwives caring for newborn babies at Rwamagana Health Facilities, and the extent to which demographic and cognitive characteristics predict nurses' HW compliance. The study’s methodology involved a cross-sectional approach encompassing descriptive and quantitative methods. The data was collected over a period of 3 weeks from a total sample of 139 nursemidwives. An anonymous self-administered questionnaire was used to collect data; out of 139 questionnaires distributed 134 were returned back completed, giving a rate of 96.4%. The data analysis was done using SPSS software version 21.The results demonstrate that a mean self-reported HW compliance rate was 82.00% (SD= 13.60). A compliance rate of 80% or greater was confirmed by most of the participants in the study (79.1%). The highest reported rate of hand washing was HW after exposure to the newborn's body fluids (M= 89.33%; SD= 14.878),while results revealed that nurses tend to perform less HW after touching an object in the vicinity of the patient(73.43%; SD=22.81). A multiple regression analysis revealed that attitudes, subjective norms and intentions were unique independent predictors of HW compliance. However, results did not show any relationship between HW and nurses' demographic characteristics.This study recommends that, strategies aiming to improve HW compliance must be focused on concepts that predict HW. Further research ought to be conducted using observational approach.

Class of 2005 Abstract / Objectives: To perform a cross-sectional survey of health care professionals and lay people on the appropriate use of antimicrobial drugs using a case-based approach. Methods: This study used a case-based survey as a tool to assess for proper antibacterial use. The survey included nine case-based scenarios involving common infectious diseases seen in the outpatient setting. Survey participants needed to assess the problem and determine if antibacterial therapy was appropriate now, not appropriate, or if the patient should be observed for 2-3 days before acting. Demographic information was also requested from the participants. Results: Thirty-one lay people and 35 professionals (pediatricians or family practice physicians) returned a completed survey. The mean (sd) cumulative scores for these two groups were 13.4 (4.45) and 21.3 (3.40), respectively. The range of scores for the lay persons was 5 to 23 and the range for the professionals was 15 to 27. The mean score for physicians was significantly greater than the mean score for lay persons. Implications: The survey results indicated that the professionals had a better understanding of the appropriate use of antibiotics versus the lay people.

No description available.

Clinical performance monitoring applications enable performance management of care processes in clinical settings. Although information technology has been advocated as a solution to support the provision of better care, the development of clinical performance monitoring applications is often a non-trivial task. A high rate of failure in IT healthcare project implementations has been reported in the literature due to the disconnect between clinicians and the development team. Furthermore, challenges inherent to the configuration of the healthcare system add to the complexity of developments. Often data sources are not adequately structured or cannot be accessed in a timely fashion; processes are uncoordinated or ill-defined; a plethora of information technologies across different healthcare organizations make interoperability problematic; and there are concerns related to privacy and security. Getting the right information to measure the achievement of the right goals at the right time for the right people is the main task to address when developing clinical performance monitoring applications. In this thesis we propose a development methodology that combines technical and managerial aspects of application development following a user-centered approach. It involves the engagement of stakeholders and users throughout in a three phase iterative process of modeling, implementation and evaluation to ensure user acceptance and adoption of applications when deployed. In particular, our focus is on the development of mobile clinical performance monitoring applications, where raw data about clinical problems are logged by healthcare providers and then transformed into meaningful reports that will support decision-making. The development methodology is evaluated using a case study of a Resident Practice Profile (RPP) application that was developed by a team lead by Dr. Gary Viner from the University of Ottawa medical school.

According to the Canadian Cancer Society, half of Ontario’s population will be diagnosed with cancer in their lifetime. Many patients being assessed for cancer however become overwhelmed when having to manage information overload, many appointments with different instructions and locations, and recommendations on how to improve their lifestyle. This causes much anxiety and uncertainty among patients. Some cancer assessment clinics offer some guidance in the form of paper-based patient navigators, which provide much reliable information to patients but are limited in terms of dynamic updates to appointments, opportunities for sharing knowledge between healthcare providers and patients, and of patients supporting each other. This thesis proposes a new web-based, mobile, and user-friendly virtual patient navigator application framework named Care Ami, which incorporates the information found in an existing paper-based navigator along with the new features such as remote updates to personal care paths and calendars, personalized navigation guidance, sharing of symptoms/medications information, and peer group support. Unlike existing solutions, Care Ami is configurable to support multiple types of diseases (e.g., lung cancer and breast cancer). This application is evaluated through testing and the usage of heuristic evaluation guidelines related to usability, and a comparison with related work highlights its many benefits.

Artificial intelligence (AI) had a few false starts – the AI winters of the 1970s and 1980s. We are now in what looks like an AI summer. There are many useful applications of AI in the field. But there are still unfulfilled promises and outright failures. From self-driving cars that work only in constrained cases, to medical image analysis products that would replace radiologists but never did, we still struggle to translate successful research into successful real-world applications. The software engineering community has accumulated a large body of knowledge over the decades on how to develop, release, and maintain products. AI products, being software products, benefit from some of that accumulated knowledge, but not all of it. AI products diverge from traditional software products in fundamental ways: their main component is not a specific piece of code, written for a specific purpose, but a generic piece of code, a model, customized by a training process driven by hyperparameters and a dataset. Datasets are usually large and models are opaque. We cannot directly inspect them as we can inspect the code of traditional software products. We need other methods to detect failures in AI products. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection