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Military veterans bring many unique and desirable traits to the workforce, including self-insight, experience, and sound judgment. Through their service in the American military branches, veterans have experience in heightened leadership roles in high stress and high stakes environments that shape their personalities, leadership skills, and behaviours. Because of this experience, veterans often demonstrate wisdom as they apply self-awareness and judgment through different ranks and roles in their service to the country. Hiring these experienced and seasoned workers to the benefit of businesses can be part of the solution in addressing the current labour shortage. Research is warranted in seeking to understand why veterans experience difficulties in their transition from the military into their business careers in higher numbers compared to the general population. The misinformation and misconceptions about hiring veterans are depriving businesses of a talented and unique population that can bring more wisdom to their workforce.

Veterans may acquire technical skills and leadership ability from their military service but have little information concerning specific entrepreneurial tasks, such as developing a business plan. This descriptive survey study examines avenues for veterans to find assistance to acquire needed skills, adding to their entrepreneurial self-efficacy. We surveyed 68 U.S. veterans who were emerging entrepreneurs, either owning their own business or indicating they intended to start their own company. Veterans’ personal attitudes and beliefs, including entrepreneurial self-efficacy, risk propensity and tolerance of ambiguity, may increase their entrepreneurial intentions. A model of veterans as emerging entrepreneurs and five propositions are offered.

While the coronavirus pandemic has affected all demographic brackets and geographies, certain areas have been more adversely affected than others. This paper focuses on Veterans as a potentially vulnerable group that might be systematically more exposed to infection than others because of their co-morbidities, i.e., greater incidence of physical and mental health challenges. Using data on 122 Veteran Healthcare Systems (HCS), this paper tests three machine learning models for predictive analysis. The combined LASSO and ridge regression with five-fold cross validation performs the best. We find that socio-demographic features are highly predictive of both cases and deaths—even more important than any hospital-specific characteristics. These results suggest that socio-demographic and social capital characteristics are important determinants of public health outcomes, especially for vulnerable groups, like Veterans, and they should be investigated further.

Accurate prediction of suicide risk is crucial for identifying patients with elevated risk burden, helping ensure these patients receive targeted care. The US Department of Veteran Affairs’ suicide prediction model primarily leverages structured electronic health records (EHR) data. This approach largely overlooks unstructured EHR, a data format that could be utilized to enhance predictive accuracy. This study aims to enhance suicide risk models’ predictive accuracy by developing a model that incorporates both structured EHR predictors and semantic NLP-derived variables from unstructured EHR. XGBoost models were fit to predict suicide risk– the interactions identified by the model were extracted using SHAP, validated using logistic regression models, added to a ridge regression model, which was subsequently compared to a ridge regression approach without the use of interactions. By introducing a selection parameter, α, to balance the influence of structured (α=1) and unstructured (α=0) data, we found that intermediate α values achieved optimal performance across various risk strata, improved model performance of the ridge regression approach and uncovered significant cross-modal interactions between psychosocial constructs and patient characteristics. These interactions highlight how psychosocial risk factors are influenced by individual patient contexts, potentially informing improved risk prediction methods and personalized interventions. Our findings underscore the importance of incorporating nuanced narrative data into predictive models and set the stage for future research that will expand the use of advanced machine learning techniques, including deep learning, to further refine suicide risk prediction methods.