A measure for assessment of beneficial and harmful fortitude: development and initial validation of the sisu scale

"It takes a lot of sisu to get through it"- managerial experiences of facing adversities during pandemic

PURPOSE

Sisu refers to extraordinary internal strength in the face of adversities. Although originally a Finnish concept, it can be a universally useful concept for studying managerial well-being and performance during difficult or even emergency periods. Previous research on sisu categorizes it into beneficial and harmful sisu and suggests that these two dimensions of sisu are useful when studying determination and resoluteness in crisis situations. This study applies the concept of sisu to examine managers' well-being in times of crisis.

METHODOLOGY

Interviews were conducted with 15 managers in the hotel, manufacturing and retail industries in Finland.

RESULTS

The beneficial effects of sisu seem to dissolve when it is used too often. The study also highlighted the paradoxical tension involved in the decision to stop or continue actions in a difficult situation and contributes to sisu research by discussing collective sisu.

CONCLUSION

The results of this study suggest that beneficial sisu helps to overcome adversity, while harmful sisu causes distortions in thinking that are detrimental to managers. Understanding one's own tendency towards beneficial or harmful sisu in difficult situations can help in managerial work. Collective sisu is an excellent resource for overcoming difficult situations in workplaces.

New insights into physician burnout and turnover intent: a validated measure of physician fortitude

BACKGROUND

Given the increasing prevalence of the physician burnout, this study provides new insights into the antecedents driving burnout and turnover intent. By introducing the concept of physician fortitude, we develop a valid and statistically-reliable measure that increases our understanding of these issues.

METHODS

A two-sample design was employed. Using a sample of 909 physicians, Advanced Practice Providers (APPs) and healthcare leaders, exploratory factor analysis was employed to create a 12-item fortitude scale. In the second study, using a sample of 212 of practicing physicians, APPs and healthcare leaders, bivariate and tetrachoric correlations, and ordinary least square regression modeling were able to establish reliability and validity.

RESULTS

The fortitude scale shows sufficient reliability. Moreover, we found significant support for convergent and criterion-related validity. Fortitude was significantly related to all three subdimensions of burnout, including emotional exhaustion (r = -.62, p < .01), depersonalization (r = -.70, p < .01) and personal accomplishment (r = .65, p < .01), and turnover intent (r = -.55, p < .01). Moreover, the fortitude measure explained more variance in all three subdimensions of burnout and turnover intent compared to common measures, including grit, hardiness, mental toughness and resilience (p < .01).

CONCLUSIONS

Results from this study empirically demonstrate that fortitude is significantly related to burnout, and turnover intent. This new fortitude measure adds a new perspective to assist in the development of more effective interventions. Opportunities for future research are discussed.

Understanding the impact of sisu on workforce and well-being: A machine learning-based analysis

This study investigates the construct of sisu, a Finnish attribute representing mental resilience and fortitude when confronted with difficult situations. By leveraging advanced analytical methods and explainable Artificial Intelligence, we gain insights into how sisu factors influence well-being, work efficiency, and overall health. We investigate how the beneficial aspects of sisu contribute significantly to mental and physical health, satisfaction, and professional accomplishments. Conversely, we analyze the harmful sisu and its adverse impacts on the same domains. Our findings, including intriguing trends related to age, educational level, emotional states, and gender, pave the way for developing tailored solutions and initiatives to nurture the beneficial aspects of sisu and curtail the damaging consequences of sisu within professional settings and personal welfare.

Investigation of different ML approaches in classification of emotions induced by acute stress

Background

Machine learning is becoming a common tool in monitoring emotion. However, methodological studies of the processing pipeline are scarce, especially ones using subjective appraisals as ground truth.

New method

A novel protocol was used to induce cognitive load and physical discomfort, and emotional dimensions (arousal, valence, and dominance) were reported after each task. The performance of five common ML models with a versatile set of features (physiological features, task performance data, and personality trait) was compared in binary classification of subjectively assessed emotions.

Results

The psychophysiological responses proved the protocol was successful in changing the mental state from baseline, also the cognitive and physical tasks were different. The optimization and performance of ML models used for emotion detection were evaluated. Additionally, methods to account for imbalanced classes were applied and shown to improve the classification performance.

Comparison with existing methods

Classification of human emotional states often assumes the states are determined by the stimuli. However, individual appraisals vary. None of the past studies have classified subjective emotional dimensions with a set of features including biosignals, personality and behavior.

Conclusion

Our data represent a typical setup in affective computing utilizing psychophysiological monitoring: N is low compared to number of features, inter-individual variability is high, and class imbalance cannot be avoided. Our observations are a) if possible, include features representing physiology, behavior and personality, b) use simple models and limited number of features to improve interpretability, c) address the possible imbalance, d) if the data size allows, use nested cross-validation.