Exploration of the Fitness of Law Enforcement Candidates across Different Ethnicities
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Sažetak

Law enforcement agencies often hire diverse candidates reflective of their community. Candidates should possess good fitness to complete academy; however, ethnicity/sociocultural norms could influence fitness. This study investigated fitness differences between law enforcement candidates of different ethnicities. Retrospective analysis was conducted on data from 2041 candidates (1659 males, 382 females) from a US law enforcement agency. During the hiring process, candidates completed: 60-s push-ups; 60-s sit-ups; 75-yard pursuit run (75PR); 60-s arm ergometer revolutions; and 2.4-km run. Candidates self-reported their ethnicity as one of the following: Asian, Black, Filipino/Pacific Islander, Hispanic, Indigenous, Non-Specified (candidate did not disclose ethnicity, ethnicity not represented in provided options), and White. A univariate analysis, with sex as a covariate, determined between-group differences (p<0.05). Effect sizes (d) were also derived for pairwise comparisons. White candidates completed fewer push-ups than Non-Specified candidates (p=0.036, d=0.28). For the 75PR, Black and Non-Specified candidates were faster than = Asian and Hispanic candidates (p≤0.024, d=0.43-0.53); White candidates were faster compared to Hispanic candidates (p<0.001, d=0.27). Black candidates completed the 2.4-km run slower than the Hispanic, Non-Specified, and White candidates (p≤0.042, d=0.22-0.43). Ethnicity alone does not determine one’s aptitude as a law enforcement officer. However, the data did suggest that certain ethnic groups exhibited select fitness differences. Personnel should understand sociocultural norms that could influence candidate fitness. This study provides further evidence for ability-based training during academy and the need for a broad fitness battery to assess different characteristics that measure the wide range of physical traits essential for performing occupational tasks.

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DOI: 10.5937/nabepo30-58478

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