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- All Subjects: Resilience
- Creators: Anderies, John M
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.
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.