Classifying Stalking Persons in South Korea into Sub-Groups Based on Urgent Emergency Measure Checklistopen access
- Authors
- Park, Eun Seo; Cho, Young Il
- Issue Date
- Jul-2025
- Publisher
- SAGE PUBLICATIONS INC
- Keywords
- stalking; stalker; latent class analysis; urgent emergency measure checklist; risk assessment; victimization
- Citation
- SAGE Open, v.15, no.3
- Indexed
- SSCI
SCOPUS
- Journal Title
- SAGE Open
- Volume
- 15
- Number
- 3
- URI
- https://scholarworks.dongguk.edu/handle/sw.dongguk/61758
- DOI
- 10.1177/21582440251381692
- ISSN
- 2158-2440
- Abstract
- This study utilized police archival data of 6,824 stalking events from January 2022 to June 2023, measured by the Urgent Emergency Measure Checklist. Latent class analysis was used to identify five subgroups and compared stalking duration and frequency, risk, request for urgent emergency measures, and reasons for non-request. The Aggression-Destruction (6.8%) displayed psychological problems, previous stalking histories, and combining stalking with violence. The Rationalizing-Noncompliance (3.5%) was characterized by rationalizing stalking behavior, and challenges in complying with or uncooperative with police instruction. The Rationalizing-Noncompliance and Aggression-Destruction had significantly high frequency and probabilities of risk and request. The Gradual (10.7%) and Shadowing (39.7%) merely hovered around the victim, with the latter having more extensive stalking histories. The Gradual had the lowest probabilities of request and necessity. The Hoverer (39.3%) threatened victims' daily lives but lacked previous stalking histories. Future research should establish stalking classification system for police investigation and develop evidence-based intervention programs tailored to each subgroup. Additionally, evaluating the effectiveness of police-clinician collaboration frameworks could enhance risk management and victim protection strategies.
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Collections - College of Police and Criminal Justice > Department of Police Administration > 1. Journal Articles

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