Health

Health data analysis has emerged as a critical domain with immense potential to revolutionize healthcare delivery, disease management, and medical research. However, it is confronted by formidable challenges, including sample bias, data privacy concerns, and the cost and scarcity of labeled data. These challenges collectively impede the development of accurate and robust machine learning models for various healthcare applications, from disease diagnosis to treatment recommendations.
Sample bias and specificity refer to the inherent challenges in working with health datasets that may not be representative of the broader population or may exhibit disparities in their distributions. These biases can significantly impact the generalizability and effectiveness of machine learning models in healthcare, potentially leading to suboptimal outcomes for certain patient groups. Data privacy and locality are paramount concerns in the era of digital health records and wearable devices. The need to protect sensitive patient information while still extracting valuable insights from these data sources poses a delicate balancing act. Moreover, the geographic and jurisdictional differences in data regulations further complicate the use of health data in a global context. Label cost and scarcity pertain to the often labor-intensive and expensive process of obtaining ground-truth labels for supervised learning tasks in healthcare. The limited availability of labeled data can hinder the development and deployment of machine learning models, particularly in specialized medical domains.

This dissertation investigates and further develops organization health theory in the context of public organizations. This is an important line of inquiry for two reasons. First, the healthy organizations literature and healthy organization theory is inchoate and lacks overall coherence (Dejoy et al., 2010), especially in public organization theory and research. As such many organization theorists have called for expansive solutions and insist this requires consideration of the collective and systemic interactive levels of analysis (Salanova et al., 2012; Schein, 2006). Second, we notice organizations now devoting considerable resources to nurturing individual and organizational health and wellness (Dale & Burrell, 2014; Parks & Steelman, 2008). Ostensibly, this is because health has been demonstrated to enhance or compromise a myriad of organizational outcomes including satisfaction, performance, sustainability, and survival (Pfeffer, 2010; Cooper, 1994). Moreover, organizational health and individual health share a vicarious and interdependent relationship (McHugh & Brotherton, 2000). In response to this “healthy exigency” and in effort to enhance the health of our public organizations, this dissertation employs an interdisciplinary lens to investigate healthy organizations at the systemic interactive level of analysis. The overarching purpose of the study is to provide theoretical contributions and empirical evidence concerning the key factors necessary for the development of healthy public organizations. To accomplish this, I assemble a holistic organizational wellness (HOW) theoretical framework. The HOW framework supports development of a Wellness Quotient (WQ) with data from the 2017 Federal Employee Viewpoint Survey (FEVS). The WQ represents the dissertations main contribution, as currently no standardized measure of public organization health (or wellness) exists. Through a process of discovery and analysis which includes multiple iterations of confirmatory factor analyses and a regression analysis, it is found that the WQ has a significant impact on organization performance and satisfaction. The results also confirm this studies hypotheses the WQ may be useful as a proxy for future healthy public organizations research. In sum, the HOW framework and WQ not only contribute to theoretical and empirical development of healthy public organizations, respectively, but they both may serve as useful tools for public organizational health design and development.