Law enforcement use of video-based technology has substantially increased over the past decade. This systematic review examines the current evidence base for efficacy of body-worn video and the current case for implementation. Five articles were identified as pertinent to this review from a search of five electronic databases, with a further six articles of grey literature included. Inter-rater reliability was high amongst three independent screeners of literature. Articles were short listed for review if they explicitly identified police and recording devices as topic areas. Articles were then excluded if they did not involve an operational trial of body-worn video. Eleven articles were included for review; of the five peer-reviewed studies, two were randomised controlled trials. An abundance of evidence was provided; however, the majority of articles were methodologically weak. Body-worn video was shown to reduce use of force incidents, crime rates for certain crime types and court costs. Public response to body-worn video was varied, as was police officer and public opinion. Due to methodological limitations evident in most studies and the general lack of peer-reviewed material, further research is required; however, there are some considerable benefits reported in the current literature.
Alprazolam rescheduling resulted in an overall reduction in alprazolam and total benzodiazepine use, without substitution with other drugs, in the short term. Unintended harms were not observed. Rescheduling appears to have been effective in reducing alprazolam use in this high-risk population.
Fairness in policing, driven by the effective and transparent investigation and remediation of police misconduct, is vital to maintaining the legitimacy of policing agencies, and the capacity for police to function within society. Research into police misconduct in Australia has traditionally been performed on an ad-hoc basis, with limited access to law enforcement data. This research seeks to identify the antecedents of serious police misconduct, resulting in the dismissal or criminal charge of officers, among a large police misconduct dataset. Demographic and misconduct data were sourced for a sample of 600 officers who have committed instances of serious misconduct, and a matched sample of 600 comparison officers across a 13-year period. A machine learning analysis, random forest, was utilised to produce a robust predictive model, with Partial Dependence Plots employed to demonstrate within variable interaction with serious misconduct. Prior instances of serious misconduct were particularly predictive of further serious misconduct, while misconduct was most prominent around mid-career. Secondary employment, and performance issues were important predictors, while demographic variables typically outperformed complaint variables. This research suggests that serious misconduct is similarly prevalent among experienced officers, as it is junior officers, while secondary employment is an important indicator of misconduct risk. Findings provide guidance for misconduct prevention policy among policing agencies.
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