Development and Validation of a Short Measure of Emotional, Physical, and Behavioral Markers of Eustress and Distress (MEDS)

Relationships between mental toughness, eustress-distress, and mindfulness in adolescents: A network analysis and mediator model testing

The main aim of this study was to investigate adolescents' eustress-distress and mindfulness by examining mental toughness (MT) using network and mediator analyses. The study included 414 adolescents. The results showed that MT was positively related to eustress and mindfulness, but distress was negatively related to MT. Based on the network analysis findings, we tested mindfulness' mediating role in the relationship between eustress-distress and MT. The findings showed that mindfulness played a significant mediating role. However, the mediating role of mindfulness was negative for the relationship between distress and toughness and positive for eustress. These findings advance eustress, distress, and mindfulness as mechanisms for understanding the effects of MT. Considering current knowledge of MT, eustress, distress, and mindfulness, the results are discussed.

A theoretical framework for Acute Care Nurse Stress Appraisal: Application of the transactional model of stress and coping

AIM

To develop a framework for understanding the stress appraisal process among acute care nurses during the COVID-19 pandemic.

DESIGN

A secondary analysis of open-ended responses from a cross-sectional survey of 3030 frontline, acute care nurses in New Jersey and the effect of burnout during the COVID-19 pandemic.

METHODS

Lazarus and Folkman's transactional model of stress and coping guided the study. Thematic analysis was used to analyse 1607 open-ended responses.

RESULTS

Nine themes emerged during the secondary appraisal of stress. Five themes contributed to distress and burnout including (1) high patient acuity with scarce resources, (2) constantly changing policies with inconsistent messaging, (3) insufficient PPE, (4) unprepared pandemic planning and (5) feeling undervalued. Four themes led to eustress and contributed to post-traumatic growth including (1) team nursing to ensure sufficient resource allocation, (2) open channels of communication, (3) sense- of-duty and (4) personal strength from new possibilities.

CONCLUSION

The COVID-19 pandemic was a traumatic event for patients and the nursing workforce. Internal and external demands placed on acute care nurses increased burnout, however, a subset of nurses with adequate support experienced personal growth.

IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE

Beyond mental health interventions for acute care nurses, organizational interventions such as reevaluation of emergency action plans to optimize resource allocation, and work environment strategies such as improved communication and decision-making transparency are necessary.

IMPACT

To better understand how frontline acute care nurses experienced stress during COVID-19, a data-informed framework was developed that included a primary and secondary appraisal of stress. Themes contributing to distress and burnout were identified, and themes leading to eustress and post-traumatic growth were also identified. These findings can assist nurse leaders in optimizing strategies to reduce burnout and promote post-traumatic growth in the post-COVID years.

REPORTING METHOD

No patient or public contribution.

Effects of Endurance Training on Antioxidant and Hormonal Status in Peripheral Blood of Young Healthy Men

(1) Background: Physical activity may cause an imbalance in the major functions of the human body. This study aimed to investigate the effects of endurance running training on the parameters of the antioxidant defense system (SOD, CAT, GPx, GR, GSH), LPO (malondialdehyde, MDA), and stress hormones (A, NA) in young healthy, previously untrained men. (2) Methods: The training program was as follows: 8 weeks of running, three times per week; the duration of a single session was 30-70 min, the intensity was twice a week in the so-called extensive endurance zone, and once a week in the anaerobic threshold zone. Blood samples were collected from the subjects, before and after the running program. (3) Results: The training program resulted in a significant increase in maximal oxygen consumption (p < 0.001). The activities of SOD, GPx, and GR also increased significantly (p < 0.05, p < 0.01, and p < 0.05, respectively), while CAT activity and GSH and MDA concentrations remained unchanged. The concentration of A decreased (p < 0.05), while the NA concentration increased significantly (p < 0.05). SOD, GPx, GR, and NA positively correlated with VO2max (p < 0.05, p < 0.001, p < 0.01, p < 0.05, respectively), while a negative correlation was detected between A and VO2max (p < 0.05). (4) Conclusions: These results indicate that there is no persistent oxidative stress in response to the applied 8-week running program, probably due to exercise-induced protective alterations in the antioxidant defense system. Furthermore, adaptations occurred at the hormonal level, making the organism more ready for a new challenge.

Navigating the decision-making landscape of AI in risk finance: Techno-accountability unveiled

The integration of artificial intelligence (AI) systems has ushered in a profound transformation. This conversion is marked by revolutionary extrapolative capabilities, a shift toward data-centric decision-making processes, and the enhancement of tools for managing risks. However, the adoption of these AI innovations has sparked controversy due to their unpredictable and opaque disposition. This study employs the transactional stress model to empirically investigate how six technological stressors (techno-stressors) impact both techno-eustress (positive stress) and techno-distress (negative stress) experienced by finance professionals and experts. To collect data for this research, an e-survey was distributed to a diverse group of 251 participants from various sources. The findings, particularly the identification and development of techno-accountability as a significant factor, contribute to the risk analysis domain by improving the failure mode and effect analysis framework to better fit the rapidly evolving landscape of AI-driven innovations.