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<p>Recruits are exposed to high levels of psychological and physical stress during the special forces selection period, resulting in dropout rates of up to 80%. To identify who likely drops out, we assessed a group of 249 recruits, every week of the selection program, on their self-efficacy, motivation, experienced psychological and physical stress, and recovery. Using linear regression as well as state-of-the-art machine learning techniques, we aimed to build a model that could meaningfully predict dropout while remaining interpretable. Furthermore, we inspected the best-performing model to identify the most important predictors of dropout. Via cross-validation, we found that linear regression had a relatively good predictive performance with an Area Under the Curve of 0.69, and provided interpretable insights. Low levels of self-efficacy and motivation were the significant predictors of dropout. Additionally, we found that dropout could often be predicted multiple weeks in advance. These findings offer novel insights in the use of prediction models on psychological and physical processes, specifically in the context of special forces selection. This offers opportunities for early intervention and support, which may ultimately improve success rates of selection programs.</p>

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Early identification of dropouts during the special forces selection program

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月25日

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  • Ruud J. R. den Hartigh,
  • Rik Huijzer,
  • Frank J. Blaauw,
  • Age de Wit,
  • Peter de Jonge

摘要

Recruits are exposed to high levels of psychological and physical stress during the special forces selection period, resulting in dropout rates of up to 80%. To identify who likely drops out, we assessed a group of 249 recruits, every week of the selection program, on their self-efficacy, motivation, experienced psychological and physical stress, and recovery. Using linear regression as well as state-of-the-art machine learning techniques, we aimed to build a model that could meaningfully predict dropout while remaining interpretable. Furthermore, we inspected the best-performing model to identify the most important predictors of dropout. Via cross-validation, we found that linear regression had a relatively good predictive performance with an Area Under the Curve of 0.69, and provided interpretable insights. Low levels of self-efficacy and motivation were the significant predictors of dropout. Additionally, we found that dropout could often be predicted multiple weeks in advance. These findings offer novel insights in the use of prediction models on psychological and physical processes, specifically in the context of special forces selection. This offers opportunities for early intervention and support, which may ultimately improve success rates of selection programs.