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Profiling the Injuries Sustained by Police Trainees Undergoing Initial Training: A Retrospective Cohort Study.

Sally SawyerBenjamin SchramRodney P PopeRobin Marc Orr
Published in: International journal of environmental research and public health (2021)
The tasks performed by police officers are unique, varied and can be performed in unexpected situations. Initial police college training is used to prepare new police officers to conduct these tasks and is known to be a time when police trainees are at an elevated risk of injury. The aim of this study was to profile injuries occurring within a national Police Force during initial training to inform injury prevention strategies. Using a retrospective cohort design, point-of-care injury data including injury body site, nature, mechanism, and the activity being performed at the time of injury were provided. A total of 564 injuries were recorded over the 22-month period, with the mean age of recruits reporting an injury being 28.83 years ± 6.9 years. The incidence of injuries ranged across training periods, from 456.25 to 3079 injuries per 1000 person-years with an overall incidence rate of 1550.15 injuries per 1000 person-years. The shoulder was the most injured site (n = 113, 20% of injuries), with sprains and strains being the most common nature of injury (n = 287, 50.9% of injuries). Muscular stress with physical exercise was the most common mechanism of injury (n = 175, 31.0% of injuries) with the activity responsible for the largest proportion of injuries being "unknown" (n = 256, 45.4% of injuries), followed by police training (n = 215, 38.1%). Injuries appear to be typically joint related-commonly the shoulder-with police training being a primary known activity at the time of injury. Prescreening protocols may be of benefit, and efforts should be made to recruit and train physically resilient trainees. Injuries, whether they occurred pre-enlistment or during training, should be fully rehabilitated prior to the individual's commencement as a qualified officer.
Keyphrases
  • virtual reality
  • risk factors
  • escherichia coli
  • single cell
  • quality improvement
  • machine learning
  • high intensity