Early identification of dropouts during the special forces selection program
Abstract 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, m...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87604-5 |
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| author | Ruud J. R. den Hartigh Rik Huijzer Frank J. Blaauw Age de Wit Peter de Jonge |
| author_facet | Ruud J. R. den Hartigh Rik Huijzer Frank J. Blaauw Age de Wit Peter de Jonge |
| author_sort | Ruud J. R. den Hartigh |
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| description | Abstract 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. |
| format | Article |
| id | doaj-art-c45bbd4971524ffba8a7eb8b5eb80196 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-c45bbd4971524ffba8a7eb8b5eb801962025-01-26T12:32:40ZengNature PortfolioScientific Reports2045-23222025-01-011511710.1038/s41598-025-87604-5Early identification of dropouts during the special forces selection programRuud J. R. den Hartigh0Rik Huijzer1Frank J. Blaauw2Age de Wit3Peter de Jonge4Department of Psychology, Faculty of Behavioural and Social Sciences, University of GroningenDepartment of Psychology, Faculty of Behavioural and Social Sciences, University of GroningenResearch and Innovation, Researchable BVHuman Performance Team Commando Corps, Ministry of DefenceDepartment of Psychology, Faculty of Behavioural and Social Sciences, University of GroningenAbstract 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.https://doi.org/10.1038/s41598-025-87604-5Gradient boostingStable and interpretable RUle setsMilitaryPersonnel selectionRecoveryStress |
| spellingShingle | Ruud J. R. den Hartigh Rik Huijzer Frank J. Blaauw Age de Wit Peter de Jonge Early identification of dropouts during the special forces selection program Scientific Reports Gradient boosting Stable and interpretable RUle sets Military Personnel selection Recovery Stress |
| title | Early identification of dropouts during the special forces selection program |
| title_full | Early identification of dropouts during the special forces selection program |
| title_fullStr | Early identification of dropouts during the special forces selection program |
| title_full_unstemmed | Early identification of dropouts during the special forces selection program |
| title_short | Early identification of dropouts during the special forces selection program |
| title_sort | early identification of dropouts during the special forces selection program |
| topic | Gradient boosting Stable and interpretable RUle sets Military Personnel selection Recovery Stress |
| url | https://doi.org/10.1038/s41598-025-87604-5 |
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