Law enforcement needs simulation practice tailored to de-escalation skills. Law enforcement officers spend most of their training time practicing how to use force appropriately and very little time practicing how to avoid using force. There is little research into the best ways for law enforcement to reduce their use of force and the best ways to provide training to individuals to coach them to avoid using force. There are many training programs promoting de-escalation techniques, but there is little research into the effectiveness of these programs beyond the ability of individuals to perform the skills that are trained. There is little to show that using these skills reduces crime, reduces the need for incarceration, reduces the level of violence, or improves the communities where they are used. The scope of this project addresses a small part of this gap by examining different ways simulators can be used to provide practice in the skills that are taught. This dissertation contributes to the field of simulation by demonstrating how virtual reality can address deficits in law enforcement training. It does so by studying which techniques are most appropriate in some scenarios and how to better train officers to use them. This project looks at different ways of allowing police officers to practice de-escalation skills to see if these have any bearing on an officer's approach to de-escalation and if the officer responds positively to the practice. This research does not attempt to take the next step of measuring the use of these skills outside the training environment. The results indicate active-duty officers have a positive response to any attempt to practice or promote de-escalation and are especially positive about the potential for training in realistic, situationally appropriate virtual environments.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2395 |
| Date | 01 January 2022 |
| Creators | Kent, Julie |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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