Prenatal care (PNC) is an essential health service that can reduce adverse health and social outcomes through prevention, detection and treatment of abnormalities of pregnancy. It offers an opportunity to mitigate the impact of the Social Determinants of Health (SDoH) on individual patients through advocacy and referral to social services. Despite a publicly funded health care system in Canada, disparities in access to PNC persist. Much is known about the barriers to PNC and client experiences of inadequate PNC (IPNC). Very little is known about care provider perspectives of IPNC, what should be done about it and are the barriers to doing it. The goal of this project was to address this gap in knowledge to inform the development of novel care delivery models that could reduce disparities in access to PNC in Hamilton. Using a Critical Theory lens, I conducted an interpretive descriptive study using individual interviews and focus groups with health and social service providers in Hamilton to explore their understandings of IPNC. Participants viewed IPNC as a small but important phenomenon disproportionately impacting people who are marginalized. The experience of IPNC is chaotic, worrisome and joyful for providers. An interdisciplinary, midwifery-led outreach PNC model would better meet the needs of the client population and providers alike. A Community Centred Care model of PNC embodies and enhances participant suggestions for addressing IPNC. Access to abortion, postpartum care and newborn apprehension require special attention. Peer participation and the impacts of patriarchy and racism must be addressed in the development of future PNC models. The ways in which participants described and proposed intervening in IPNC revealed an individualized understanding of the SDoH that is paralleled in existing research on IPNC. This conceptualization of the problem obscures the root causes of disparities in access and warrants future consideration. / Thesis / Master of Science (MSc) / Prenatal care (PNC) can help to reduce complications of pregnancy and birth and connect expecting families with other support services. Even though health care is free in Canada, people who are marginalized struggle to access enough PNC. We know the complexities of people’s lives and their negative experiences with the health care system prevent them from getting PNC. We know little about what PNC for people who don’t access it is like for the providers or why they can’t make their care easier to access. Mindful of systems of power, the goal of this research is to explore how health and social service providers understand inadequate PNC (IPNC), to inform accessible PNC models. Participants proposed an interdisciplinary outreach PNC model responsive to needs of the community. The Social Determinants of Health were an important part of how participants understood IPNC which shaped the way that they proposed to address it.

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Musculoskeletal trauma including fractures, represents a significant burden of disease for Low- and Middle-Income Countries (LMICs). Within LMICs, women possess reduced agency to make health care decisions and represent a vulnerable population. In this thesis, I aimed to characterize priority fractures among women within LMICs, and investigated whether women were delayed in hospital admission following an orthopaedic trauma. In Chapter 1, I introduce and review the existing literature on injury burden, health care deficiencies, and gender inequities within LMICs. In Chapter 2, we analyzed regional distributions of fracture burdens across 9,934 female orthopaedic trauma patients across 17 LMICs. Half of our study patients were ≥ 60 years old. We determined that the major burden of orthopaedic trauma among women within LMICs were fractures among the elderly. Fracture burden in Africa was notably different. A majority of patients were between the ages 18-59, and common fractures included tibia/fibula and femur fractures. In Chapter 3, we analyzed 26,910 orthopaedic trauma patients across 17 LMICs to determine whether women were delayed in hospital admission by >24 hours. After controlling for confounds, sex was not a significant predictor of delay. We found that instead, the severity and type of fracture influenced the delay of patient’s hospital admission. Closed fractures, falling-related injuries, pelvic, spine and hip fractures were associated with increasing delay. Irrespective of sex and region, inter-hospital referrals accounted for nearly half of the reasons patients were delayed. These two chapters highlight regional trends in orthopaedic burden sustained by women, pointing to the high frequency of fragility fractures. In addition, this thesis identifies critical gaps within LMICs’ health care systems infrastructure, demonstrating the need for improved hospital referral systems and ambulatory services. This analysis will enable policymakers, and future researchers to target interventions to address the rising global burden of injuries especially among women. / Thesis / Master of Science (MSc) / Fractures represent life-threatening injuries within Low- and Middle-Income Countries (LMICs), and globally are a top-ten leading cause of death and disability. Within LMICs, due to gender inequalities, women may be restricted from receiving hospital care following an injury. We investigated the most common types of fractures in women within LMICs and determined that women most frequently experienced fractures due to old age. We further examined whether women were delayed in reaching a hospital after sustaining a fracture, and found that sex did not significantly play a role in determining delay. Instead, injury associated factors, such as the type and severity of the fracture influenced whether a patient was delayed. In addition, transferring patients between hospitals was the most common reason for delay. As a result, policymakers in LMICs should explore strategies to treat the high burden of fractures in the elderly and improve communication between hospitals to reduce delays.

This study showed that health equity must be achieved through education, awareness, and advocacy. A structured program must be put in place to provide accountability towards achieving health equity within organizations, communities, cites, and states. In Virginia, the Health Equity Campaign was a program put in place to provide such accountability to the citizens of Virginia. This study attempted to evaluate the Health Equity Campaign implemented by the Virginia Department of Health Office of Minority Health and Public Health Policy Division of Health Equity in order to get all Virginians to become advocates for health equity in their organizations, communities, neighborhoods. Organizational/group leaders were interviewed in addition to surveying various staff members. This study provides a detailed description of the strength of the Health Equity Campaign's ability to promote education and awareness about health equity and why many participants found it difficult to transition from motivation to advocacy. / Ph. D.

<p dir="ltr">Families experiencing homelessness and housing instability (FEH/HI) face myriad challenges, placing their children at risk for adverse outcomes. Research typically adopts a deficit-based approach to meet immediate needs, but this may limit understanding of the children’s experiences. Recognizing children’s strengths is important for improving their health, development, and support. Resilience and family resilience are concepts that draw on a strengths-based approach. However, there is a need for more research to identify the strengths, such as resilience, among families and their children who are not stably housed. The present research seeks to address the gaps in the literature by examining the resilience perceptions and experiences of adolescents in FEH/HI, as much of the existing research focuses on the adult perspectives, within a family resilience framework through multiple studies. Study 1 (Chapter 2) systematically reviewed existing research on resilience and family resilience within FEH/HI. An analysis of 27 studies identified resilience-related factors across individual, interpersonal, and community domains. Study 2 (Chapter 3) integrated a strengths- and deficit-based approach to explore the challenges and strengths of children in FEH/HI from the perspectives of parents/caregivers and service providers. Semi-structured interviews with 17 parents/caregivers and 15 service providers identified challenges and strengths at the individual, interpersonal, and system levels. Study 3 (Chapter 4) investigated how adolescents within FEH/HI experience and make meaning of family resilience in their day-to-day lives using photo-elicitation (PE) and Froma Walsh’s family resilience framework. Four adolescents participated and took photographs depicting family resilience within their families. Together, findings from these studies provide insights into the strengths and resilience within FEH/HI. Moreover, they emphasize the need for strengths-based approaches in research and practice to support the health, development, and wellbeing of children and adolescents in FEH/HI.</p>

The prevalence of big data has rapidly changed the usage and mechanisms of data analytics within organizations. Big data is a widely-used term without a clear definition. The difference between big data and traditional data can be characterized by four Vs: velocity (speed at which data is generated), volume (amount of data generated), variety (the data can take on different forms), and veracity (the data may be of poor/unknown quality). As many industries begin to recognize the value of big data, organizations try to capture it through means such as: side-channel data in a manufacturing operation, unstructured text-data reported by healthcare personnel, various demographic information of households from census surveys, and the range of communication data that define communities and social networks. Big data analytics generally follows this framework: first, a digitized process generates a stream of data, this raw data stream is pre-processed to convert the data into a usable format, the pre-processed data is analyzed using statistical tools. In this stage, called statistical learning of the data, analysts have two main objectives (1) develop a statistical model that captures the behavior of the process from a sample of the data (2) identify anomalies in the process. However, several open challenges still exist in this framework for big data analytics. Recently, data types such as free-text data are also being captured. Although many established processing techniques exist for other data types, free-text data comes from a wide range of individuals and is subject to syntax, grammar, language, and colloquialisms that require substantially different processing approaches. Once the data is processed, open challenges still exist in the statistical learning step of understanding the data. Statistical learning aims to satisfy two objectives, (1) develop a model that highlights general patterns in the data (2) create a signaling mechanism to identify if outliers are present in the data. Statistical modeling is widely utilized as researchers have created a variety of statistical models to explain everyday phenomena such as predicting energy usage behavior, traffic patterns, and stock market behaviors, among others. However, new applications of big data with increasingly varied designs present interesting challenges. Consider the example of free-text analysis posed above. There's a renewed interest in modeling free-text narratives from sources such as online reviews, customer complaints, or patient safety event reports, into intuitive themes or topics. As previously mentioned, documents describing the same phenomena can vary widely in their word usage and structure. Another recent interest area of statistical learning is using the environmental conditions that people live, work, and grow in, to infer their quality of life. It is well established that social factors play a role in overall health outcomes, however, clinical applications of these social determinants of health is a recent and an open problem. These examples are just a few of many examples wherein new applications of big data pose complex challenges requiring thoughtful and inventive approaches to processing, analyzing, and modeling data. Although a large body of research exists in the area of anomaly detection increasingly complicated data sources (such as side-channel related data or network-based data) present equally convoluted challenges. For effective anomaly-detection, analysts define parameters and rules, so that when large collections of raw data are aggregated, pieces of data that do not conform are easily noticed and flagged. In this work, I investigate the different steps of the data analytics framework and propose improvements for each step, paired with practical applications, to demonstrate the efficacy of my methods. This paper focuses on the healthcare, manufacturing and social-networking industries, but the materials are broad enough to have wide applications across data analytics generally. My main contributions can be summarized as follows: • In the big data analytics framework, raw data initially goes through a pre-processing step. Although many pre-processing techniques exist, there are several challenges in pre-processing text data and I develop a pre-processing tool for text data. • In the next step of the data analytics framework, there are challenges in both statistical modeling and anomaly detection o I address the research area of statistical modeling in two ways: - There are open challenges in defining models to characterize text data. I introduce a community extraction model that autonomously aggregates text documents into intuitive communities/groups - In health care, it is well established that social factors play a role in overall health outcomes however developing a statistical model that characterizes these relationships is an open research area. I developed statistical models for generalizing relationships between social determinants of health of a cohort and general medical risk factors o I address the research area of anomaly detection in two ways: - A variety of anomaly detection techniques exist already, however, some of these methods lack a rigorous statistical investigation thereby making them ineffective to a practitioner. I identify critical shortcomings to a proposed network based anomaly detection technique and introduce methodological improvements - Manufacturing enterprises which are now more connected than ever are vulnerably to anomalies in the form of cyber-physical attacks. I developed a sensor-based side-channel technique for anomaly detection in a manufacturing process / PHD / The prevalence of big data has rapidly changed the usage and mechanisms of data analytics within organizations. The fields of manufacturing and healthcare are two examples of industries that are currently undergoing significant transformations due to the rise of big data. The addition of large sensory systems is changing how parts are being manufactured and inspected and the prevalence of Health Information Technology (HIT) systems in healthcare systems is also changing the way healthcare services are delivered. These industries are turning to big data analytics in the hopes of acquiring many of the benefits other sectors are experiencing, including reducing cost, improving safety, and boosting productivity. However, there are many challenges that exist along with the framework of big data analytics, from pre-processing raw data, to statistical modeling of the data, and identifying anomalies present in the data or process. This work offers significant contributions in each of the aforementioned areas and includes practical real-world applications. Big data analytics generally follows this framework: first, a digitized process generates a stream of data, this raw data stream is pre-processed to convert the data into a usable format, the pre-processed data is analyzed using statistical tools. In this stage, called ‘statistical learning of the data’, analysts have two main objectives (1) develop a statistical model that captures the behavior of the process from a sample of the data (2) identify anomalies or outliers in the process. In this work, I investigate the different steps of the data analytics framework and propose improvements for each step, paired with practical applications, to demonstrate the efficacy of my methods. This work focuses on the healthcare and manufacturing industries, but the materials are broad enough to have wide applications across data analytics generally. My main contributions can be summarized as follows: • In the big data analytics framework, raw data initially goes through a pre-processing step. Although many pre-processing techniques exist, there are several challenges in pre-processing text data and I develop a pre-processing tool for text data. • In the next step of the data analytics framework, there are challenges in both statistical modeling and anomaly detection o I address the research area of statistical modeling in two ways: - There are open challenges in defining models to characterize text data. I introduce a community extraction model that autonomously aggregates text documents into intuitive communities/groups - In health care, it is well established that social factors play a role in overall health outcomes however developing a statistical model that characterizes these relationships is an open research area. I developed statistical models for generalizing relationships between social determinants of health of a cohort and general medical risk factors o I address the research area of anomaly detection in two ways: - A variety of anomaly detection techniques exist already, however, some of these methods lack a rigorous statistical investigation thereby making them ineffective to a practitioner. I identify critical shortcomings to a proposed network-based anomaly detection technique and introduce methodological improvements - Manufacturing enterprises which are now more connected than ever are vulnerable to anomalies in the form of cyber-physical attacks. I developed a sensor-based side-channel technique for anomaly detection in a manufacturing process.

Purpose: Social determinants of health (SDoH) and rurality have both been shown to contribute to severe maternal morbidity (SMM). Appalachian communities often embody this compounded risk, but regional SMM is under-explored. This study’s purpose is to explore SMM in rural areas of Appalachian states. Aims: There are two specific aims. 1) Describe the prevalence of rural SMM in Appalachian states. 2) Investigate the relationship between SMM and SDoH, specifically income, education, and care access. Methods: An IRB exempt, descriptive correlational study was conducted. Birth data (2018-2022) were extracted from the CDC WONDER database for 12 Appalachian states. Demographic, income, and education data were obtained from the U.S. Census. Access was measured by March of Dimes (MoD) maternity care categorizations. Descriptive statistics and Pearson’s correlations were conducted in IBM SPSS. Results: Rural SMM rates correlated with poverty (r =.803, p Conclusions: This study describes rural SMM in Appalachian states, showing correlation with poverty, education, and maternity care access. Limited access to care is correlated with a higher SMM burden for rural areas. This points to the need for further exploration into rural SMM, and the interplay of SDoH and geography in relation to maternal health.

This study explored doctor of physical therapy (DPT) students’ attitudes, perceived knowledge, and perceived competence specific to social and structural determinants of health (SDOH). Current students in a DPT program housed within a college of public health in an urban research institution served as the participants for this research. The primary purpose of this study was to explore students’ self-evaluation and perceptions of competence with SDOH in hopes of gaining insight into elements of their education that contributed to their preparedness and/or what strategies and resources are needed to foster competence in this area. A secondary aim of this study was to explore how individual student factors and curricular factors impact students’ awareness of SDOH. A mixed methods study design employed bivariate and multivariate analysis of participants’ responses to self-report Likert scale survey questions and analysis of semi-structured interviews using qualitative description and phenomenological principles. Quantitative data analysis revealed differences in perceived skills competence based on cohort (year one, two, or three in the program) with first-year students demonstrating lower perceived competence. Analysis of attitudes and knowledge demonstrated that all participants held a positive perspective regarding the importance of SDOH as well as perceived foundational knowledge for this content. Quantitative analysis also detected subtle differences in specific sample beliefs and behaviors based on demographic variables such as gender identity, race, and first-generation student status. Qualitative data supported the quantitative findings with participants articulating specific elements of their identities and the DPT curriculum that contributed to their understanding of SDOH. An iterative coding process identified two primary themes that corresponded to the research aims: 1) Learners’ perceived importance of social and structural determinants of health and factors that impact how to address them; and 2) Learning about social and structural determinants of health: What learners bring with them and what they gain throughout the curriculum. These findings shed light on the elements of this educational program that foster students understanding of SDOH and the types of experiences that help clinical learners appreciate the impact of these upstream drivers of health for patients and populations. / Policy, Organizational and Leadership Studies