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Background <p>Police cadets undergo persistent and elevated stress due to continuous training and evaluation. Identifying resilience and risk factors in this population can thus crucially inform management decisions within the police force. Here, in two large cohorts of police cadets (<i>n</i> = 1069, 30% women and <i>n</i> = 1377, 35% women) we investigated whether broad personality traits could predict internalizing symptoms (somatization, depression, and anxiety) as well as mental health-related quality of life (MHRQoL). Moreover, we compared seven popular artificial intelligence and linear regression models (Elastic Net, General Linear Model, Lasso Regression, Neural Networks, Random Forests, and Support Vector Regression) in predicting MHRQoL as a function of all other variables.</p> Results <p>A Random Forest accounted for about half of the observed variance in MHRQoL, and outperformed all other models by up to 12% in an out-of-sample cross-validation. In all analyses, emotional stability emerged as the primary personality trait linked to MHRQoL, with anxiety and somatization symptoms partially mediating this relationship.</p> Conclusions <p>Our findings delineate the personality factors that best predict internalizing symptoms and MHRQoL among cadets, and tentatively suggest that Random Forest models might be a powerful forecasting tool in police management.</p>

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Personality predicts internalizing symptoms and quality of life in police cadets: a comparison of artificial intelligence and parametric approaches

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月4日

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  • Macià Buades-Rotger,
  • Ana Martínez Catena,
  • Guillermo Recio,
  • Mireia Cano Gallent,
  • Jordi Niñerola i Maymí,
  • Anna Figueras Masip,
  • David Gallardo-Pujol

摘要

Background

Police cadets undergo persistent and elevated stress due to continuous training and evaluation. Identifying resilience and risk factors in this population can thus crucially inform management decisions within the police force. Here, in two large cohorts of police cadets (n = 1069, 30% women and n = 1377, 35% women) we investigated whether broad personality traits could predict internalizing symptoms (somatization, depression, and anxiety) as well as mental health-related quality of life (MHRQoL). Moreover, we compared seven popular artificial intelligence and linear regression models (Elastic Net, General Linear Model, Lasso Regression, Neural Networks, Random Forests, and Support Vector Regression) in predicting MHRQoL as a function of all other variables.

Results

A Random Forest accounted for about half of the observed variance in MHRQoL, and outperformed all other models by up to 12% in an out-of-sample cross-validation. In all analyses, emotional stability emerged as the primary personality trait linked to MHRQoL, with anxiety and somatization symptoms partially mediating this relationship.

Conclusions

Our findings delineate the personality factors that best predict internalizing symptoms and MHRQoL among cadets, and tentatively suggest that Random Forest models might be a powerful forecasting tool in police management.