一种超分析性预测方法,用于测量特种作战部队候选人与健身相关的学率
Alain Dössegger1,2, Thomas Gsponer3, Christian Protte1
1Swiss Federal Institute of Sport Magglingen SFISM, Magglingen 2532, Switzerland.
Military medicine
|January 7, 2026
概括
在健身评估后向特种作战部队 (SOF) 候选人提供培训建议,与历史数据相比,与健身相关的学率显著降低了30%以上.
科学领域:
- 军事科学 军事科学
- 人类性能优化优化 人类性能优化
- 特别行动部队 (SOF) 的选拔.
背景情况:
- 特别行动部队 (SOF) 选择中的高磨损率通常归因于身体条件不足和与训练相关的伤害.
- 在准备阶段实施有针对性的培训策略可以减轻这些问题.
研究的目的:
- 评估个性化培训建议在减少SOF候选人中与健身相关的学率方面的有效性.
- 为了比较当前的退学率与历史的退学数据.
主要方法:
- 一项前性观察研究,涉及来自瑞士武装部队和警察部队的男性SOF候选人.
- 候选人接受了个性化的培训指导,以及体能评估后的培训指导.
- 使用元分析预测方法的贝叶斯分析将观察到的退学率与历史对照进行了比较.
主要成果:
- 与健身相关的学影响了29%的参与者,比历史平均水平42%下降.
- 这意味着学业学的相对减少超过了30%.
- 91.6%的后面概率表明了降低的学率 (odds比率<1).
结论:
- 作为人类绩效管理的一部分,个性化培训建议可以有效地降低SOF候选人的与健身相关的学率.
- 这种方法可以提高候选人保留率和在苛刻的选择过程中的成功率.
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