Filtering by
- All Subjects: Resilience
- Creators: Chen, Angela
- Creators: Chester, Mikhail
Quantum resilience makes two very important claims. First, resilience cannot be characterized without recognizing both the system and the valued function it provides. Second, resilience is not about disturbances, insults, threats, or perturbations. To avoid crippling infinities, characterization of resilience must be accomplishable without disturbances in mind. In light of this, quantum resilience defines resilience as the extent to which a system delivers its valued functions, and characterizes resilience as a function of system productivity and complexity. System productivity vis-à-vis specified “valued functions” involves (1) the quanta of the valued function delivered, and (2) the number of systems (within the greater system) which deliver it. System complexity is defined structurally and relationally and is a function of a variety of items including (1) system-of-systems hierarchical decomposition, (2) interfaces and connections between systems, and (3) inter-system dependencies.
Among the important features of quantum resilience is that it can be implemented in any system engineering tool that provides sufficient design and specification rigor (i.e., one that supports standards like the Lifecycle and Systems Modeling languages and frameworks like the DoD Architecture Framework). Further, this can be accomplished with minimal software development and has been demonstrated in three model-based system engineering tools, two of which are commercially available, well-respected, and widely used. This pragmatic approach assures transparency and consistency in characterization of resilience in any discipline.
Motivated by the need for cities to prepare and be resilient to unpredictable future weather conditions, this dissertation advances a novel infrastructure development theory of “safe-to-fail” to increase the adaptive capacity of cities to climate change. Current infrastructure development is primarily reliant on identifying probable risks to engineered systems and making infrastructure reliable to maintain its function up to a designed system capacity. However, alterations happening in the earth system (e.g., atmosphere, oceans, land, and ice) and in human systems (e.g., greenhouse gas emission, population, land-use, technology, and natural resource use) are increasing the uncertainties in weather predictions and risk calculations and making it difficult for engineered infrastructure to maintain intended design thresholds in non-stationary future. This dissertation presents a new way to develop safe-to-fail infrastructure that departs from the current practice of risk calculation and is able to manage failure consequences when unpredicted risks overwhelm engineered systems.
This dissertation 1) defines infrastructure failure, refines existing safe-to-fail theory, and compares decision considerations for safe-to-fail vs. fail-safe infrastructure development under non-stationary climate; 2) suggests an approach to integrate the estimation of infrastructure failure impacts with extreme weather risks; 3) provides a decision tool to implement resilience strategies into safe-to-fail infrastructure development; and, 4) recognizes diverse perspectives for adopting safe-to-fail theory into practice in various decision contexts.
Overall, this dissertation advances safe-to-fail theory to help guide climate adaptation decisions that consider infrastructure failure and their consequences. The results of this dissertation demonstrate an emerging need for stakeholders, including policy makers, planners, engineers, and community members, to understand an impending “infrastructure trolley problem”, where the adaptive capacity of some regions is improved at the expense of others. Safe-to-fail further engages stakeholders to bring their knowledge into the prioritization of various failure costs based on their institutional, regional, financial, and social capacity to withstand failures. This approach connects to sustainability, where city practitioners deliberately think of and include the future cost of social, environmental and economic attributes in planning and decision-making.
In a healthcare system already struggling with burnout among its professionals, the COVID-19 pandemic presented a barrage of personal and occupational strife to US healthcare workers. Structural and everyday discrimination contributed to the health inequities of people of color in the US, exacerbated by COVID-19-related racism and xenophobia. There is little research regarding the effects of COVID-19 and related and/or concurring discrimination upon minority nursing staff, despite their importance in supporting the diverse American patient population with culturally competent, tireless care amid the pandemic. This cross-sectional survey study aimed to examine 1) the relationships between discrimination, social support, resilience, and quality of life among minority nursing staff in the US during COVID-19, and 2) the differences of discrimination, social support resilience, and quality of life among minority nursing staff between different racial/ethnic groups during COVID-19. The sample (n = 514) included Black/African American (n = 161, 31.4%), Latinx/Hispanic (n = 131, 25.5%), Asian (n = 87, 17%), Native American/Alaskan Native (n = 69, 13.5%), and Pacific Islander (n = 65, 12.7%) nursing staff from 47 US states. The multiple regression results showed that witnessing discrimination was associated with a lower quality of life score, while higher social support and resilience scores were associated with higher quality of life scores across all racial groups. Furthermore, while participants from all racial groups witnessed and experienced discrimination, Hispanic/Latinx nursing staff experienced discrimination most commonly, alongside having lowest quality of life and highest resilience scores. Native American/Alaskan Native nursing staff had similarly high discrimination and low quality of life, although low resilience scores. Our findings suggest that minority nursing staff who have higher COVID-19 morbidity and mortality rates (Hispanic/Latinx, Native American/Alaskan Native) were left more vulnerable to negative effects from discrimination. Hispanic/Latinx nursing staff reported a relatively higher resilience score than all other groups, potentially attributed to the positive effects of biculturality in the workplace, however, the low average quality of life score suggests a simultaneous erosion of well-being. Compared to all other groups, Native American and Alaskan Native nursing staff’s low resilience and quality of life scores suggest a potential compounding effect of historical trauma affecting their well-being, especially in contrast to Hispanic/Latinx nursing staff. This study has broader implications for research on the lasting effects of COVID-19 on minority healthcare workers’ and communities’ well-being, especially regarding Hispanic/Latinx and Native American/Alaskan Native nursing staff.
Method: Twenty families with children 10 years and older were recruited to participate in a 3-week equine assisted learning program at a therapeutic riding center in Phoenix, Arizona. Sessions included groundwork activities with horses used to promote life skills using experiential learning theory. The study design included a mixed-method quasi-experimental one-group pretest posttest design using the following mental health instruments: Devereaux Student Strengths Assessment, Brief Family Assessment Measure (3 dimensions), and Family Satisfaction Scale to measure child social-emotional competence, family function, and family satisfaction, respectively. Acceptability was determined using a Likert-type questionnaire with open-ended questions to gain a qualitative thematic perspective of the experience.
Results: Preliminary pretest and posttest comparisons were statistically significant for improvements in family satisfaction (p = 0.001, M = -5.84, SD = 5.63), all three domains of family function (General Scale: p = 0.005, M = 6.84, SD = 9.20; Self-Rating Scale: p = 0.050, M = 6.53, SD = 12.89; and Dyadic Relationship Scale: p = 0.028, M = 3.47, SD = 7.18), and child social-emotional competence (p = 0.015, M = -4.05, SD 5.95). Effect sizes were moderate to large (d > 0.5) for all but one instrument (Self-Rating Scale), suggesting a considerable magnitude of change over the three-week period. The intervention was highly accepted among both children and adults. Themes of proximity, self-discovery, and regard for others emerged during evaluation of qualitative findings. Longitudinal comparisons of baseline and 3-month follow-up remain in-progress, a topic available for future discussion.
Discussion: Results help to validate equine assisted learning as a valuable tool in the promotion of child social-emotional intelligence strengthened in part by the promotion of family function and family satisfaction. For mental health professionals, these results serve as a reminder of the alternatives that are available, as well as the importance of partnerships within the community. For therapeutic riding centers, these results help equine professionals validate their programs and gain a foothold within the scientific community. Additionally, they invite future riding centers to follow course in incorporating evidence into their programs and examining new directions for growth within the mental health community.