This study addressed the long-standing crisis of Black severe maternal morbidity in the U.S., while the COVID-19 pandemic led to even worse outcomes. The purpose of the study was to identify significant predictors of the quality of patient-provider relationships during a birth hospitalization. The sample of Black women (N=182) gave moderate ratings for quality of patient provider relationships, and for level of trust, rapport, and communication with providers. Providers were rated as having a fair level of cultural sensitivity, competence, and humility—while 30.2% rated them as poor. For experiences of racism, discrimination and inequities in service delivery, combining categories of a “few times” and “many times,” 53.3% felt racially stereotyped or treated like a racial stereotype, 52.5% were treated with less respect than a White woman would have been, 39.7% were verbally abused or yelled at, 43.8% were scolded, ridiculed, mocked, and shamed, 47.2% felt belittled and put down, 42.7% felt threatened, coerced, lied to, and manipulated, and 46% felt their pain was not managed the same way as for a White woman. Women entered the hospital with risk factors of cardiovascular disease (20.3%), hypertension (23.6%), obesity (18.1%), and diabetes (13.7%). Further, 74.2% had COVID-19 in the past two years, 25.8% had long COVID-19, 34.1% had COVID-19 during their pregnancy, and 34.1% had COVID-19 at delivery. Medical events during their delivery hospitalization included hemorrhage (40.7%), blood clot (25.3%), and a hypertensive disorder of pregnancy (25.3%). Women had high rates (over 75%) of past year depression, anxiety, and trauma—with 68.1% receiving counseling; and higher rates (over 85%) the year post-partum—with 76.9% receiving counseling. Noteworthy significant predictors of a higher quality of patient-provider relationships were higher education, higher trust/ rapport/ communication with providers, and lower global racism/ discrimination/ inequities during service delivery—while entering the hospital with lower risk factors for pregnancy-related complications (69.8% of variance predicted). The study contributes to literature on the crisis of severe maternal morbidity for Black women in the U.S, as well as factors that need to be addressed to reduce it, while offering a cache of culturally appropriate measures for ongoing research.

Objective. The purpose of this study is to examine the relationship between the COVID-19-related waivers and the number of Summer Food Service Program (SFSP) meals served, accessibility of SFSP sites, and implementation of the SFSP sponsored by the Office of Food and Nutrition Service (OFNS) of the New York City Department of Education (NYC DOE). Methods. This study is a convergent parallel mixed methods study. In the quantitative component, there are two research questions (“research question 1” and “research question 2”); the design is a non-experimental, one-group, completely within-subjects design; and the unit of analysis is NYC DOE geographic districts (n = 32). Research question 1 is “Among NYC DOE geographic districts, was there a significant difference in the number of SFSP meals served during the summers when the COVID-19-related waivers were used compared to the summers without the waivers?” Research question 2 is “Among NYC DOE geographic districts, was there a significant difference in the accessibility of SFSP sites during the summers when the COVID-19-related waivers were used compared to the summers without the waivers?” Both research questions 1 and 2 compare the first summer of the waivers (2020) to the six summers prior to the waivers (2014-2019) and the second summer of the waivers (2021) to the six summers prior to the waivers (2014-2019). In the qualitative component, there is one research question (“research question 3”); the methods consist of a document analysis of the policy memos for the waivers (n = 8) using the READ approach for document analysis of health policies. Research question 3 is “What were the intended relationships between the COVID-19-related waivers and SFSP participation, site accessibility, and implementation according to the policy memos for the waivers?” Data Analysis. For research question 1, the statistical tests are the repeated-measures analysis of variance (ANOVA) omnibus test and post-hoc analysis with Bonferroni adjustment. The primary outcome is the total number of SFSP meals served per student. For research question 2, the statistical tests are the repeated-measures ANOVA omnibus test and post-hoc analysis with the Bonferroni adjustment when the full sample is analyzed (n = 32), and the Friedman test and sign test with the Bonferroni adjustment when high poverty districts (n = 16), high non-White districts (n = 16), and high enrollment districts (n = 16) are analyzed. The primary outcome is the number of SFSP sites per 1,000 students. For research question 3, the analysis consists of deductive coding, inductive coding, and identification of themes. Results. For research question 1, the results show a significant increase in the number of SFSP meals served per student during the first summer of the waivers compared to summers 2016-2019 (p ≤ 0.01). However, there were no significant differences in the number of SFSP meals served per student during the second summer of the waivers compared to summers 2014-2019. Among the secondary outcomes, there was a significant increase in the number of breakfast meals served in August per student during both the first and second summer of the waivers compared to summers 2014-2019 (p < 0.05). For research question 2, the results show a significant decrease in the number of SFSP sites per 1,000 students during the first summer of the waivers compared to summers 2014-2019 (p < 0.01). Similarly, there was a significant decrease during the second summer of the waivers compared to summers 2015-2019 (p < 0.01). For research question 3, the results show that the Meal Service Time Flexibility Waiver may address pre-pandemic barriers in the SFSP, but the Parent/Guardian Meal Pickup Waiver may cause implementation issues. Conclusions. Among NYC DOE geographic districts, the waivers may increase the reach of breakfast meals served in August while decreasing the number of SFSP sites and making SFSP implementation easier. There is a need for a pilot study or more controlled study to establish causal relationships. Policymakers should consider making the Meal Service Time Flexibility Waiver and the Non-Congregate Feeding Waiver permanent flexibilities for summer meal programs.

In the wake of the COVID-19 pandemic, New York City (NYC) emerged as a global epicentre, revealing stark disparities in its impact across diverse neighbourhoods and populations. This dissertation delves into the uneven geographies of the pandemic city, critically examining the paradoxes, linkages, and questions embedded in the infrastructures that shape and are shaped by the politics of the city. As modern life becomes increasingly intertwined with complex digital control systems, these infrastructures, far from being rational, orderly or even intelligible, obscure systems of power that govern their stable flow and circulation. Drawing on Stephen Graham’s concept of infrastructural “disruption”, this research sheds light on how everyday infrastructures—often invisible until they fail—reveal intricate tensions between distance and access, between participation and criminalisation, and between mobility and class. Through a multi-scaled empirical analysis, this research delves deeper into the topological and topographical characteristics of urban infrastructure during a time of crisis to illuminate their role in mediating relationships between citizens, space and justice in our everyday lives. This dissertation is anchored around three categories of spatial unevenness: geographies of access, geographies of digital participation, and geographies of work. Three infrastructural modalities are interrogated during the COVID-19 moment in NYC: the built environment, a digital governance platform, and the personal mobile phone. The study seeks to answer pivotal questions regarding access to critical pandemic response infrastructure, patterns of civic participation in NYC’s 311 non-emergency hotline, and the spatial politics of remote work behaviour. Ultimately, by unmasking the intricate web of infrastructural politics, this research offers an in-depth understanding of the disproportionate impacts of the COVID-19 spread and emphasises the significance of spatial considerations in our theorisations of justice.

This dissertation consists of three chapters, each containing a distinct research paper in the field of macroeconomics. In the first chapter, I estimate the impact of mutual fund flows on corporate bond prices, issuance and firm investment. I leverage variation caused by the COVID-19 induced financial panic of March 2020 and find that safer firms suffered a larger impact in the component of bond spreads that does not compensate for expected default risk. However, I do not detect impacts of fund flows on issuance or investment. A simple model predicts liquidation decisions and price responses as being driven by demand and liquidation elasticities, which depend on the characteristics of the bond return processes. In the second chapter, we ask: what is the importance of firm and bank credit factors in determining investment responses to monetary policy? We decompose variation in corporate loan growth rates into purely firm-level and bank-level variation. The estimated factors are correlated with a set of variables that proxy for the firm’s and bank’s financial health. Firms with a higher borrowing factor experience relatively larger investment responses to an unexpected interest rate shock; the effect is muted when the shock is the reveal of central bank information. The bank factor does not induce similar heterogeneity in investment responses. In the third chapter, we ask: what is the nature of optimal monetary policy and central bank disclosure when the monetary authority is uncertain about the economic state? We consider a model in which firms make nominal pricing decisions and the central bank sets the nominal interest rate under incomplete information. We find that implementing flexible-price allocations is both feasible and optimal despite the existence of numerous measurability constraints; we explore a series of different implementations. When monetary policy is sub-optimal, public information disclosure by the central bank is welfare-improving as long as either firm or central bank information is sufficiently precise.

Higher education institutions (HEIs) have faced unprecedented challenges during the Covid-19 pandemic. This dissertation draws on the comparative case study design to examine the institutional response to the Covid-19 pandemic across seventeen HEIs in three urban contexts: Hong Kong, Johannesburg, and New York. Due to the limited knowledge base about the novel coronavirus and its rapid spread, the institutional response to the Covid-19 pandemic was premised on uncertainty and presented a unique challenge to decision-makers. This study is informed by the systems approach in the three strands of literature I draw from – disaster studies, sociology of risk, and higher education governance. The evidence from this study supports the conceptualization of the Covid-19 response as a by-product of social design and socially constructed events. I take a qualitative approach to study the institutional response through semi-structured interviews, documents, and recruitment survey responses. Guided by organized risk sensemaking, I put forth the following research questions: (1) What policies, if any, have been adopted to mitigate the risk of Covid-19? (2) What decision-making structures, if any, have been mobilized to mitigate the risk of Covid-19? (3) How, if at all, institutional managers have rationalized the decisions adopted in response to the Covid-19 pandemic? and (4) How, if at all, has the external environment impacted the institutional response to Covid-19? In the first findings chapter, I examine the Covid-19 policies adopted during the pandemic and conclude that the measures taken to mitigate risks associated with the pandemic have counter-intuitive consequences. The Covid-19 response has strengthened HEIs’ place-based identity and underscored the role universities and colleges play in their immediate communities as anchor institutions. The second analytical chapter shows how decision-making structures were established and mobilized during the Covid-19 pandemic within different HEIs. It typifies decision making structures by their focus (general vs. specific) and temporality (permanent vs. temporary). This chapter discusses the challenges and benefits of different decision-making approaches, including the involvement of faculty and staff, the elimination of organizational silos, and the funneling of decisions to higher levels of authority. Furthermore, I interrogate the institutional managers’ rationalizations of challenges and ethical dilemmas brought on by the pandemic. In this chapter, I present the four emerging attitudes toward the Covid-19 pandemic as a sensemaking framework, illuminating the institutional response as a temporally dynamic phenomenon. Lastly, I focus on the external environment and specifically, the non-state sectoral actors that have played a crucial role in informing and shaping HEIs' responses. The relationships with these actors serve advisory, brokerage, coordination, data collection, material support, lobbying, and translation functions for HEIs. The study contributes to the literature on comparative education by providing empirical evidence on the role of non-state sectoral actors, the decision-making processes of HEIs, and the impact of Covid-19 on higher education. It also highlights the importance of universities and colleges as anchor institutions within their communities.

In this thesis, we consider improving supply chain and service systems through demand-side management. In Chapters 1 and 2, we focus on a new notion of flexibility that has emerged in e-commerce called consumer flexibility. Motivated by the fact that some customers may willingly provide flexibility on which product or service they receive in exchange for a reward, firms can design flexible options to leverage this consumer flexibility for significant benefit in their operations. In Chapter 1, we consider the context of online retailing where consumer flexibility can be realized through opaque selling, where some specific attributes of the products are not revealed to the customer until after purchase. In Chapter 2, we focus on the context of online booking systems for scheduled services where consumer flexibility can be realized through large time windows. The main findings are on the power of limited flexibility using simple flexible options with just a small fraction of customers willing to be flexible. In Chapter 3, we study the issue of congested elevator queuing systems due to the requirement of social distancing during a pandemic. We propose simple interventions for safely managing the elevator queues, which require no programming of the elevator system and only manage passenger behaviors. The key idea is to explicitly or implicitly group passengers going to the same or nearby floor into the same elevator as much as possible. Simulations and stability analysis show that our proposed interventions significantly reduce queue length and wait time.

While the mental health impacts of the COVID-19 pandemic on the general U.S. public health workforce have been well described, the effects of the COVID-19 response on Black, Indigenous, and People of Color (BIPOC) working in public health have not been adequately characterized. BIPOC public health professionals may have suffered, potentially, greater stress and more negative health impacts during the pandemic due to being part of communities experiencing severe COVID-19 health inequities and the potential for racism-related stress in the workplace. This study utilized a cross-sectional design to investigate the associations between risk factors/predictors and higher levels of burnout among BIPOC public health professionals working during the COVID-19 pandemic. Survey data was collected using the Qualtrics survey platform and SPSS was used for data analysis. Survey items measured multiple domains including professional experience (i.e., years of experience, job functions, hours worked, volunteer work), mental and physical health status (i.e., co-morbidities, BMI, COVID-19 diagnosis, insomnia, anxiety, depression, trauma, burnout), professional and personal stress (before and during the pandemic), and racism-related stress (i.e., discrimination, harassment, heightened vigilance, cultural taxation). Of the total respondents (n = 486), 80% experienced insomnia, 68.5% experienced depression, 81.7% experienced anxiety and 61.3% experienced trauma. BIPOC public health professionals suffered a moderately high overall level of burnout (mean = 2.578, SD = 0.486, min = 1, max = 3.9) and a high level of exhaustion (mean = 2.744, SD = 0.532, min = 1, max = 4). Paired t-tests found respondents’ physical and mental health status were each significantly worse during the pandemic (p < .000). Respondents also had significantly worse professional and personal stress during the pandemic (p < .000). Backward stepwise regression found higher burnout significantly predicted by: not having sought counseling; lower rating of mental health during COVID-19; higher past year mental distress (i.e., depression, anxiety, insomnia and trauma); higher past month perceived stress; and higher vigilance. These findings emerge as important in informing the public health field regarding the current and future needs of BIPOC public health professionals during the pandemic and beyond.

Over the course of the Coronavirus pandemic, works of art explored social isolation, abolition, and climate crisis. The pandemic had ruptured normative curricular practices in schools and learning discourses focused on minimizing those interruptions. Meanwhile, works such as Ellen Reid’s SOUNDWALK, Kamau Ware’s Fighting Dark, and Maya Lin’s Ghost Forest crafted relationships to knowledge through site-specific sounds, familiar materials, and sensory experiences of their environments. A group of curriculum designers, researchers, and educators, including the author of this study, affiliated with a university-based Curriculum Lab engaged with these artworks, while processing the pandemic’s effects on their own curricular practices. Situated within the Lab, this project used ethnographic and speculative methods to research how the artworks’ aesthetic and sensory strategies activated curricular contact zones and contributed to artful practices for curriculum theory and design. This study built on the work of critical curriculum scholarship which has demonstrated that significant forms of knowledge and belonging are produced through informal and null curriculum, and outside of schools entirely. Drawing on aesthetics, affect, and vital materialisms, this study theorized ambient curriculum: a surround through which any variety of onto-epistemological practices might cohere into relationships of knowing and becoming. At the same time, this study recognized that formal curriculum exerts a large influence on the daily lives of teachers and students, and that there are educators searching for forms of curriculum more aligned to their commitments to social and ecological justice; beliefs about the complexity of knowledge and learning; and approach to design as a creative process. This project considers the implications of such creative processes for curriculum design as a nomadic practice and curriculum designers as nomadic becomings, making and made by their creations.

Algorithmic management systems organize many different kinds of work across domains, and have increasingly come under academic scrutiny. Under labels including gig work, piecemeal work, and platform labor, these systems have been richly theorized under disciplines including human-computer interaction, sociology, communications, economics, and labor law. When it comes to the relationships between such systems and their workers, current theory frames these interactions on a continuum between organizational control and worker autonomy. This has laid the groundwork for other ways of examining micro-level practices of workers under algorithmic management. As an alternative to the binary of control and autonomy, this dissertation takes its cue from feminist scholars in Science, Technology, and Society (STS) studies. Drawing on frameworks from articulation, repair, and mutual shaping, I examine workers’ interpretations and interactions, to ask how new subjectivities around identity and community emerge from these entanglements. To shed empirical light on these processes, this dissertation employs a mixed-methods research design examining the introduction of facial recognition into the sociotechnical systems of algorithmic management. Data include 22 in-person interviews with workers in New York City and Toronto, a survey of 100 workers in the United States who have been subjected to facial recognition, and analysis of over 2800 comments gathered from an online workers’ forum posted over the course of four years.Facial recognition, like algorithmic management, suffers from a lack of empirical, on-the-ground insights into how workers communicate, negotiate, and strategize around and through them. Interviews with workers reveals that facial recognition evokes polysemia, i.e. a number of distinct, yet interrelated interpretations. I find that for some workers, facial recognition means safety and security. To others it means violation of privacy and accusations of fraud. Some are impressed by the “science-fiction”-like capabilities of the system: “it’s like living in the future.” Others are wary, and science fiction becomes a vehicle to encapsulate their fears: “I’m in the [movie] The Minority Report.” For some the technology is hyper-powerful: “It feels like I’m always being watched,” yet others decry, “it’s an obvious façade.” Following interviews, I build a body of research using empirical methods combined with frameworks drawn from STS and organizational theory to illuminate workers’ perceptions and strategies negotiating their algorithmic managers. I operationalize Julian Orr’s studies of storytelling among Xerox technicians to analyze workers’ information-sharing practices in online forums, to better understand how gig workers, devices, forums, and algorithmic management systems engage in mutual shaping processes. Analysis reveals that opposing interpretations of facial recognition, rather than dissolving into consensus of “shared understanding,” continue to persist. Rather than pursuing and relying on shared understanding of their work to maintain relationships, workers under algorithmic management, communicating in online forums about facial recognition, elide consensus. After forum analysis, I then conduct a survey, to assess workers’ fairness perceptions of facial recognition targeting and verification. The goal of this research is to establish an empirical foundation to determine whether algorithmic fairness perceptions are subject to theories of bounded rationality and decision-making. Finally, for the last two articles, I turn back to the forums, to analyze workers’ experiences negotiating two other processes with threats or ramifications for safety, privacy, and risk. In one article, I focus on their negotiation of threats from scam attackers, and the use the forum itself as a “shared repertoire” of knowledge. In the other I use the forums as evidence to illuminate workers’ experiences and meaning-making around algorithmic risk management under COVID-19. In the conclusion, I engage in theory-building to examine how algorithmic management and its attendant processes demand that information-sharing mechanisms serve novel ends buttressing legitimacy and authenticity, in what I call “para-organizational” work, a world of work where membership and legitimacy are liminal and uncertain. Ultimately, this body of research illuminates mutual shaping processes in which workers’ practices, identity, and community are entangled with technological artifacts and organizational structures. Algorithmic systems of work and participants’ interpretations of, and interactions with, related structures and devices, may be creating a world where sharing information is a process wielded not as a mechanism of learning, but as one of belonging.

Over the past decade, and accelerated due to the COVID-19 pandemic, there has been increasing adoption of decentralized diagnostic testing, where the testing is brought closer to the patient. This trend has largely been fueled by the development of more accurate diagnostic tools and faster and more reliable data connectivity. Decentralized testing has been shown to greatly reduce turnaround times while increasing accessibility to users in remote regions. However, there are challenges that limit its widespread adoption. In this dissertation, we detail the development of tools and technologies to overcome these barriers and expedite the shift towards decentralized diagnostic testing. First, we demonstrate the ability to develop point-of-care (POC) diagnostic tests with performance that rivals that of traditional lab-based methods. We developed a rapid, multiplexed, microfluidic serological test for Lyme disease, a tick-borne disease caused by the Borrelia burgdorferi bacterium. The recommended testing, the standard 2-tiered (STT) approach, is not sensitive for early-stage infections, is labor-intensive, has long turnaround times, and requires the use of two immunoassays (enzyme-linked immunosorbent assay (ELISA) and the Western Blot). We developed a standalone multiplexed sandwich ELISA assay and adapted it to the mChip microfluidic platform. We validated the assay on a rigorously characterized panel of human serum samples and demonstrated that our approach outperforms the STT algorithm on sensitivity while matching its specificity. The form factor of this technology is amenable to use in physician’s offices and urgent care clinics. We also showed exploratory work towards adapting the mChip platform for diagnosis of Zika disease, a mosquito-borne disease caused by the Zika virus, and acute kidney injury, a syndrome characterized by loss of kidney excretory function. Next, we worked on increasing the adoption of rapid diagnostic tests for self- and partner-testing designed to be used in at-home settings. We developed a smartphone application to be used alongside the INSTI Multiplex test for detecting HIV and syphilis infections. The application was designed to provide users with i) instructions on running the test, ii) an automated deep-learning-based image interpretation algorithm to interpret the rapid test results from a smartphone image, iii) a way to save test results and display/share them, and iv) resources for follow-up care. We adopted a user-centered, iterative design process where we worked with a cohort of study participants composed of men who have sex with men and transgender women at high risk for contracting sexually transmitted infections. We then field tested the application with 48 participants over a duration of three months and found high acceptability for the application, both in terms of functionality and helpfulness. Finally, we sought to address a key limitation with deep-learning-based image classification techniques, specifically, the requirement for large numbers of annotated images for training. We developed a deep-learning image interpretation algorithm that could be quickly adapted to new rapid test kits using only a fraction of the images that would otherwise be needed for training the model. The interpretation algorithm followed a three-step, modular process. First, the rapid test kit and the membrane were extracted from the smartphone image. Second, the constituent zones were cropped from the extracted membrane. Finally, a classifier detected the presence or absence of a line in the individual zones. Fast adaptation was demonstrated by adapting a base model, trained using images of a single COVID-19 rapid test kit, to four different rapid test kits, each with different form factors, using few-shot domain adaptation. After training with 20 or fewer images, the classification accuracies of all the adapted models were > 95%. This approach can provide a digital health platform for improved pandemic preparedness and enable quality assurance and linkage to care for consumers operating new LFAs in widespread decentralized settings. Together, these methods provide a suite of tools that could expedite the shift towards decentralized, POC testing.