Classifying Stalking Persons in South Korea into Sub-Groups Based on Urgent Emergency Measure Checklist

Stalking and Police Response in South Korea

Stalking is a pervasive crime in South Korea. In 2022, reports of stalking cases surged to 29,565, more than ten times the 2,767 cases reported in 2018, when the police established the stalking code in the 112 system (Korean National Police Agency, 2022). The increase was most pronounced in 2021 when the Anti-stalking Act was enacted. About 80% of stalkers were men, while 80% of victims were women (Korean National Police Agency, 2023). The relationships between stalkers and victims also align with previous studies, which found that intimate relationships were the most common and stranger relationships were the least familiar (Miller, 2012). In particular, the ex-lover accounted for over 60%, the highest percentage (Korean National Police Agency, 2023).

With the enactment of the Anti-stalking Act, South Korean police reinforced their response to stalking. When someone reports stalking to 112, the police must respond to the scene and assess the risk using the UEMC. Based on the result, the police can take emergency or urgent emergency measures. Emergency measures include warning stalkers of punishment, investigating crimes, and guiding victims to shelters. Urgent emergency measures are taken when repeated stalking is a concern, prohibiting stalkers from approaching within 100 meters of the victim and from contacting them via telecommunications. Both victims and police can apply for this measure. If the recurrence of stalking remains a concern, the prosecutor or police can detain the stalkers in a detention center through provisional measures.

Until 2022, 1,671 emergency measures and 2,073 provisional measures were implemented (Korean National Police Agency, 2022). Since these measures require approval from the police, prosecution, and court, it takes an average of 2 to 3 days, sometimes as long as a week. Even if the measure is approved, 10% of them are violated, resulting in fines. Although police strengthened their response to stalking, the immediate deterrent effect is low for stalkers due to the slight punishment.

Stalking Subgroups in South Korea

In South Korea, two recent studies have attempted to classify subgroups of stalkers empirically. These studies primarily targeted individuals convicted of stalking and focused on various stalking behaviors of the convicts stated in the judgment. Each study categorized stalking: shadowing, surveillance, and gradual stalking (Kang & Kim, 2021); online, approach, and attack stalking (Park & Lee, 2022). Although these subgroups are distinct, they share characteristics. Integrating both studies, a typology of South Korean stalking emerges that includes (1) stalking without direct contact with the victims, (2) stalking with a combination of direct and indirect contact with the victim, and (3) stalking that involves directly attacking or harming the victim. We identified these three subgroups, and integrate them with the stalking typology discussed in previous studies.

The first subgroup includes shadowing stalking (Kang & Kim, 2021) and online stalking (Park & Lee, 2022). These stalkers follow the victim’s daily routine or contact them online rather than directly. They do not specifically face the victim or attempt to cause physical harm. This behavior is typical of early-stage stalking. However, if such early-stage stalking behavior persists for more than 2 weeks, they may become more intrusive and threatening to victims (Purcell et al., 2004, 2005).

The second subgroup includes surveillance stalking (Kang & Kim, 2021) and approach stalking (Park & Lee, 2022). These stalkers follow or contact the victim online and also visit the victim in person. They make contact, tail, or watch the victim. This stalking is more severe than that of the first subgroup. Notably, the rate of mental illness among stalkers in this subgroup is relatively high, which is particularly concerning (Park & Lee, 2022). While stalkers with mental illness are not necessarily at high risk of violence (Mullen et al., 1999), those with personality-disordered are more likely to exhibit violent tendencies (Schwartz-Watts & Morgan, 1998).

The third subgroup includes gradual stalking (Kang & Kim, 2021) and attack stalking (Park & Lee, 2022), who are the most dangerous. These stalkers did not respond to initial rejection appropriately and developed into an aggressive type with long-term persistence. They go beyond stalking and engaging in violent crimes to harm their victims. They often have previous criminal histories and are frequently former lovers or spouses of victims. Among the RECON typology (Mohandie et al., 2006), the intimate stalkers (type I) are considered the most dangerous subgroup, as they frequently engage in violence throughout the course of stalking (Farnham et al., 2000; Racine & Billick, 2014; Tjaden, 1998).

Sample

The data for this study originated from the South Korean National Police Agency and consisted of stalking report records in the 112 system. No personal identifiers were included, and there was no direct contact with human participants. Risk were minimized by using only de-identified data, and IRB approval was obtained (DUIRB2024-06-19).

Table 1 presents the demographic characteristics of stalkers and victims. The data comprised 6,824 stalking incidents reported to police from January 2022 to June 2023. Most cases were reported by calling 112 (n = 6,194, 90.8%), and some were reported to the police in person (n = 388, 5.7%). Most stalkers were men (n = 5,567, 81.6%), while most victims were women (n = 2,580, 77.4%). Stalkers were typically former romantic partners or spouses of the victims (n = 4,164, 61.0%). In addition, there were also family relationships (n = 147, 2.2%), acquaintances (friends, co-workers, neighbors, etc.; n = 27.6, 28.0%), and even relationships that they did not know at all (n = 548, 8.0%). Over half of stalking cases were reported within approximately 1 month of the first known stalking event (n = 4,228, 62.0%). On average, it took 3.31 months (SD = 11.61) to report. Approximately half of all stalking was reported following the first known occurrence. The highest rate of occurrence among stalking cases was over 100 times a year, with an average rate of 52.75 times a year across cases 53.72 (SD = 92.56). Urgent measures were required in 24.0% of the cases where UEMC scores were four or higher. The average UEMC score was approximately 2.25 (SD = 1.87). Victims requested urgent emergency measures in 24.2% of cases, which reflected the necessity for urgent emergency measures across cases (24%). For cases in which urgent emergency measures were not requested, the reasons for not request were also investigated. “Other reasons” were the most common, and there were reasons such as “the stalker was not identified” and “a later decision.” The most common reason for not requesting urgent emergency measures, excluding “other reasons,” was that the stalking was considered a matter of no consequence (n = 281, 4.1%).

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Table 1.

Descriptive Statistics on Stalking Reported to the Police.

(N = 6,824)
Variable		n	%	M	SD
Victim’s gender	Male	1,544	22.6
	Female	5,280	77.4
Stalking person’s gender	Male	5,567	81.6
	Female	1,257	18.4
Relationship	ex-lover or ex-spouse	4,164	61.0
	Family	147	2.2
	Acquaintance	1,885	27.6
	Stranger	548	8.0
Stalking duration (months)	1 month or less	4,228	62.0	3.31	11.61
	3 months or less	1,072	15.7
	6 months or less	539	7.9
	1 year or less	373	5.5
	3 years or less	327	4.8
	Over 3 years	101	1.5
Number of stalking occurrences in a year	The first occurrence	3,098	45.4	53.72	92.56
	Under 10 times	808	11.8
	Under 50 times	986	14.4
	Under 100 times	352	5.2
	Over 100 times	1,515	22.2
Risk of recidivism	Yes	1,635	24.0	2.25	1.87
	No	5,189	76.0
Request forUrgent emergency Measures	Yes	1,649	24.2
	No	5,175	75.8
Reason for non-request	Fear of reprisal	122	1.8
	A matter of no consequence	800	11.7
	No expectation of action effectiveness	281	4.1
	Other	3,061	44.9

Note. Percentages of reason for non-request do not sum to 100% due to exclusion of missing or uncategorized responses. “Other” responses include reasons such as “the stalker was not identified” and “a later decision.”

Latent Classes of Urgent Emergency Measure Checklist

We fit a series of LCA models, progressively increasing the number of classes by one for each subsequent model. Table 2 presents fit indices for the LCA models with two through six latent classes. The AIC, BIC, and ssBIC all exhibit a decreasing trend as the number of latent classes increases, decreasing with slight variation on the five-class models. The five-class solution exhibits the highest entropy value, indicating distinct class separations based on UEMC items. These findings endorse the five-class solution as the best-fitting model for delineating stalker subgroups.

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Table 2.

Fit Indices for LCA Models with 2 to 6 Classes.

No. of classes	AIC	BIC	ssBIC	p-value ofBLRT	Entropy
2	58,534.45	58,704.78	58,625.33	<.001	.63
3	57,860.52	58,119.41	57,998.66	<.001	.65
4	57,647.30	57,994.76	57,832.69	<.001	.66
5	57,513.98	57,950.01	57,746.64	<.001	.70
6	57,423.13	57,947.73	57,703.04	<.001	.61

Note. The boldface indicates the preferred model for a given fit index.

LCA = latent class analysis; AIC = Akaike information criterion; BIC = Bayesian information criterion; ssBIC = sample-size adjusted BIC; BLRT = bootstrap likelihood ratio test.

Figure 1 displays item probability plots for the five-class model and shows the class distributions for the sample, providing insight into the likelihood of specific class members endorsing UEMC items. These items help differentiate latent classes and offer a descriptive overview of the five identified subgroups based on their profiles. The probabilities for each item by class are also presented as percentages in Table 3. We sorted the classes according to the risk level based on class-specific probabilities for each item, and discussed the detailed characteristics of each class accordingly.

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Figure 1.

Profile of five latent classes—Conditional item probability based on class.

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Table 3.

Profile of Five Latent Classes—Conditional Item Probability Based on Class.

		Class 4:Aggression-Destruction 6.8%		Class 1:RationalizingNon-compliance 3.5%		Class 3:Gradual 10.7%		Class 5:Hoverer 39.3%		Class 2:Shadowing 39.7%
Items		Prob	SE	Prob	SE	Prob	SE	Prob	SE	Prob	SE
1.	The intimidation or assault from the stalking person	0.85***	0.06	0.49***	0.07	0.24***	0.02	0.37***	0.02	0.04***	0.01
2.	Exposure of victim’s residence	0.99***	0.01	0.93***	0.07	0.88***	0.02	0.87***	0.01	0.38***	0.02
3.	Daily life difficulties due to stalking	0.85***	0.03	0.71***	0.11	0.37***	0.03	0.41***	0.02	0.05***	0.01
4.	Concerns about escalating stalking	0.69***	0.03	0.46***	0.12	0.13***	0.02	0.18***	0.01	0.00	0.00
5.	Alcohol and drug problems	0.35***	0.03	0.33***	0.05	0.07***	0.01	0.09***	0.01	0.00	0.00
6.	Psychiatric diagnosis history	0.23***	0.04	0.09**	0.03	0.06***	0.01	0.04***	0.01	0.01**	0.00
7.	Suicide attempts or mentions	0.40***	0.06	0.10*	0.05	0.04***	0.01	0.07***	0.01	0.00	0.00
8.	Violation of prior protective measures	0.07***	0.02	0.03	0.03	0.14***	0.02	0.00	0.00	0.00	0.00
9.	Prior history of stalking reported in the APO systems	0.23***	0.04	0.27***	0.07	0.79***	0.05	0.00	0.00	0.04***	0.01
10.	A combination of stalking and other crimes	0.55***	0.04	0.49***	0.08	0.14***	0.02	0.18***	0.01	0.01	0.01
11.	Attempts to rationalize stalking	0.42***	0.07	0.92***	0.10	0.12***	0.02	0.15***	0.01	0.02**	0.01
12.	Challenges in complying with or uncooperative with police instructions.	0.15*	0.08	0.89***	0.22	0.05***	0.01	0.07***	0.01	0.02***	0.00

Note. APO system = Anti-Abuse Police Officers system.

*

p < .05 **p < .01 ***p < .001.

Class 4 accounted for 6.8% of the sample and was characterized by high probabilities across most items, suggesting the highest risk profile. Class 4 exhibited a high likelihood of assaulting or threatening their victims, along with a high probability of intensifying their stalking behavior, thereby threatening victims’ daily lives (e.g., Intimidation or assault from the stalker [Item 1], exposure of the victim’s residence [Item 2], Difficulties in daily life due to stalking [Item 3], and Concerns about escalating stalking [Item 4]). Furthermore, Class 4 demonstrated severe psychological and behavioral issues (e.g., alcohol and drug problems [Item 5], psychiatric diagnosis history [Item 6], and suicide attempts or mentions [Item 7]). Importantly, Class 4 showed the highest possibility of escalating stalking behaviors into violent crimes (e.g., A combination of stalking and other crimes [Item 10]). Class 4 represents the highest-risk profile among all classes and is labeled Aggression–Destruction.

Class 1 accounted for 3.5% of the sample and was characterized by high probabilities on items related to the stalkers’ attempts to rationalize stalking behavior [Item 11] and challenges in complying with or being controlled by police instructions [Item 12]. Additionally, the likelihood of combining stalking with other crimes was the second highest among the classes. Besides, Class 1 was characterized by low probabilities of having psychological problems (e.g., alcohol and drug problems [Item 5] and mentioning or attempting suicide [Item 7]). This finding suggests that individuals in Class 1 are more prone to rationalizing their actions and engaging in additional criminal activities. Furthermore, they display non-cooperative behavior towards law enforcement. Therefore, we called Class 1 Rationalizing Non-compliance.

Class 3 accounted for 10.7% of the sample and was characterized by high probabilities on several indicators related to previous stalking behaviors (e.g., stalker’s violation of protective measures [Item 8], prior history of stalking in the Anti-Abuse Police Officers (APO) systems [Item 9]). Like Class 2, the likelihood of psychological or behavioral issues was low. We designated Class 3 Gradual.

Lastly, Class 5 comprised 39.3% and exhibited a relatively high probability for items related to threatening the victim’s daily life than other items. Similar to Class 4, Class 5 stalkers showed characteristics of ongoing stalking behavior but lacked previous histories (e.g., Previous history of stalking in the APO system [Item 9]). Thus, while Class 5 stalkers threaten their victims’ daily lives, reporting on their stalking behavior is in the early stage or has not yet occurred. Therefore, Class 5 is labeled as Hoverer.

Class 2 comprised 39.7% of the sample and was characterized by the lowest probabilities across all items. Particularly noteworthy was the absence of concerns regarding the combination of stalking with other criminal activities and their psychological or behavioral issues (e.g., a combination of stalking and other crimes [Item 10], alcohol and drug problems [Item 5], history of psychiatric diagnosis [Item 6]). The likelihood of rationalizing stalking was also very low. Additionally, individuals in Class 2 did not demonstrate uncooperative behavior toward police instructions. All stalkers in this class only knew the victims’ residence [Item 2]. In summary, Class 2 was characterized by a low-risk profile, with minimal potential for progression to other criminal activities such as violence. Hence, we called Class 2 Shadowing.

Distal Outcomes

Table 4 reports the relationship between the identified profiles and outcome variables (duration, frequency, necessity, request, and reasons for non-request), as illustrated in Figures 2 through 4. In addition, overall chi-square tests and pairwise comparisons are provided (see Table 5).

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Table 4.

Mean and Standard Error of the Outcome Variables for each Latent Class.

	Outcome variables	Class 4:Aggression-Destruction		Class 1:Rationalizing-Noncompliance		Class 3:Gradual		Class 5:Hoverer		Class 2:Shadowing
Duration		5.10	0.75	3.66	0.83	3.57	2.47	4.00	0.48	1.55	0.33
Frequency		128.59	7.73	87.84	9.72	49.96	3.61	61.64	2.30	19.36	1.99
Necessity		0.88	0.02	0.81	0.04	0.14	0.03	0.11	0.01	0.04	0.01
Request		0.85	0.02	0.79	0.04	0.16	0.03	0.14	0.02	0.03	0.01
Reason	Fear of reprisal	0.15	0.04	0.16	0.07	0.02	0.01	0.04	0.01	0.00	0.00
For	A matter of no consequence	0.01	0.02	0.03	0.03	0.16	0.02	0.13	0.01	0.32	0.02
Non-request	No expectation of effectiveness	0.04	0.03	0.20	0.07	0.07	0.01	0.07	0.01	0.07	0.01
	Other	0.80	0.05	0.61	0.11	0.76	0.03	0.77	0.01	0.61	0.02

Note. Analyses were performed with BCH and DCAT procedures in Mplus 8. Relations between five latent classes and categorical outcomes (necessity, request, fear of reprisal, a matter of no consequence, no expectation of effectiveness, and other) are presented as probability, standard error (SE), and for continuous outcomes (duration and frequency), Mean and SE.

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Table 5.

Chi-square Difference Test among Five Latent Classes to the Outcome Variable.

Difference test			Duration	Frequency	Necessity	Request	Reason for non-request
Overall test			32.97***	382.79***	1913.67***	1780.51***	287.37***
Class 1	vs.	Class 2	5.57*	47.97***	340.84***	298.32***	62.37***
		Class 3	1.25	13.18***	248.79***	178.84***	13.75**
		Class 4	1.47	9.22**	2.49	1.35	4.69
		Class 5	0.12	6.75**	283.99***	217.56***	11.33*
Class 2	vs.	Class 3	4.03*	51.72***	15.38***	23.28***	29.68***
		Class 4	20.17***	193.12***	1423.21***	1162.78***	210.12***
		Class 5	12.33***	141.81***	20.40***	28.61***	86.24***
Class 3	vs.	Class 4	0.32	78.19***	656.14***	494.95***	35.99***
		Class 5	1.05	7.24**	1.29	1.26	6.10
Class 4	vs.	Class 5	1.35	62.28***	1196.65***	857.22***	36.12***

Note. Analyses were performed with BCH and DCAT procedures in Mplus 8. The degree of freedom for the overall test is 4, and the group difference test is 1.

*

p < .05. **p < .01. ***p < .001.

The classes significantly differed in duration, χ²(4) = 32.97, p < .001. The Rationalizing Non-compliance subgroup (Class 1) demonstrated significantly different scores on this variable compared to individuals in the Shadowing subgroup (Class 2). Similarly, the Shadowing subgroup displayed significant score discrepancies compared to the Gradual subgroup (Class 3), the Aggression–Destruction subgroup (Class 4), and the Hoverer subgroup (Class 5). Among these subgroups, the Aggression–Destruction subgroup exhibited the most prolonged duration (M = 5.10, SE = 0.75). Additionally, the duration was sequentially longer in the Hoverer (M = 4.00, SE = 0.48), Rationalizing Non-compliance (M = 3.66, SE = 0.83), Gradual (M = 3.57, SE = 2.47), and Shadowing subgroups (M = 1.55, SE = 0.33).

The classes also differed significantly in frequency, χ²(4) = 382.79, p < .001, with all subgroups demonstrating statistically significant differences in scores from each other. The Aggression–Destruction subgroup showed the highest frequency among the subgroups (M = 128.59, SE = 7.73). Additionally, the frequency was sequentially higher in the Rationalizing Non-compliance (M = 87.84, SE = 9.72), Hoverer (M = 61.64, SE = 2.30), Gradual (M = 49.96, SE = 3.61), and Shadowing subgroups (M = 19.36, SE = 1.99).

The necessity varied significantly across all subgroups, χ²(4) = 1913.67, p < .001. Specifically, the Rationalizing Non-compliance subgroup’s scores differed significantly from those of the Shadowing, Gradual, and Hoverer subgroups. Similarly, the Shadowing subgroup’s scores differed significantly from those of the Gradual, Aggression–Destruction, and Hoverer subgroups. The Gradual subgroup differed significantly from the Aggression–Destruction subgroup. Lastly, the Aggression–Destruction differed significantly from the Hoverer subgroup. Notably, the Aggression–Destruction subgroup displayed the highest probability of necessitating urgent emergency measures (prob = .88, SE = 0.02), suggesting a heightened risk profile. Sequentially, the probability of necessity was higher in the Rationalizing Non-compliance subgroup (prob = .81, SE = 0.04), followed by the Gradual (prob = .14, SE = 0.03), Hoverer (prob = .11, SE = 0.01), and Shadowing subgroups (prob = .04, SE = 0.01).

Similarly, the request varied significantly across all subgroups, χ²(4) = 1,780.51, p < .001. Specifically, the Rationalizing Non-compliance subgroup demonstrated statistically different scores compared to the Gradual, Gradual, and Hoverer subgroups. The Shadowing group’s scores differed significantly from those of the Gradual, Aggression–Destruction, and Hoverer groups. Additionally, the Gradual subgroup differed from the Hoverer subgroup. Finally, the Aggression–Destruction subgroup differed from the Hoverer subgroup. Again, the Aggression–Destruction subgroup showed the highest probability of requesting urgent emergency measures (prob = .88, SE = 0.02). The probability of request followed a similar sequence: Rationalizing Non-compliance (prob = .81, SE = 0.04), Gradual (prob = .14, SE = 0.03), Hoverer (prob = .11, SE = 0.01), and Shadowing (prob = .04, SE = 0.01). The findings regarding the request closely mirrored those of the overall necessity result.

Furthermore, we investigated the probability of reasons for not requesting urgent emergency measures, which varied significantly among all subgroups, χ²(4) = 287.37, p < .001. Specifically, the Rationalizing Non-compliance subgroup’s scores differed significantly from those of the Gradual, Gradual, and Hoverer subgroups. The Shadowing group’s scores differed significantly from those of the Gradual, Aggression–Destruction, and Hoverer subgroups. Additionally, the Gradual subgroup differed from the Hoverer subgroup, and the Aggression–Destruction subgroup differed from the Hoverer subgroup. Excluding the “other” response, victims of stalkers in the Rationalizing Non-compliance and Aggression–Destruction subgroups primarily cited fear of reprisal as the reason for not requesting urgent emergency measures. In addition, victims of stalkers in the Rationalizing Non-compliance subgroup also cited low expectations of the measures’ effectiveness. Conversely, victims of stalkers in the Shadowing, Gradual, and Hoverer subgroups primarily indicated that they did not perceive stalking as a matter of consequence.

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Figure 2.

Mean of duration and frequency for each latent class.

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Figure 3.

Probability of necessity and request for each latent class.

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Figure 4.

Probability of reasons for non-request of each latent class.

Strengths and Limitations

Our study expanded the scope of samples used in stalking research. Parkhill et al. (2022) noted that stalking research has largely been limited to forensic/clinical samples and specific groups like college students, raising concerns about whether findings from such samples can be generalized to stalkers within the criminal justice system. To address this ambiguity, we utilized a broader and more diverse sample of individuals involved in stalking. Additionally, much of the existing research has focused on populations that are Western, educated, industrialized, rich, and democratic (WEIRD) populations (Chung & Sheridan, 2022), leaving questions about whether findings are applicable to non-Western contexts. By examining the heterogeneity of stalking behaviors in South Korea, we aimed to contribute to a more comprehensive understanding of stalking that extends beyond these traditional populations.

However, our study has some limitations. First, the Urgent Emergency Measure Checklist used for LCA comprises three subfactors: risk perceived by the victim, psychological history of the offender, and the risk evaluated by the police. If a stalker flees the scene and remains unidentified, the police may fail to confirm the stalker’s personal information, resulting in an inability to score them on some items. Approximately 70% of items, including the stalker’s psychiatry diagnoses, drug abuse, and suicide attempts, could not be confirmed. Therefore, these items need to be revised to make them applicable without identifying the stalker’s personal information and to assess the stalker’s risk accurately. Secondly, we could not determine whether the same stalker had been reported multiple times in the dataset. Specifically, for high-frequency subgroups such as the Rationalizing Non-compliance or Aggression–Destruction subgroups, the same stalker has likely been reported numerous times. Future studies should collect data that enable the identification of repeatedly reported stalkers.

Future Direction

Our findings suggest the need for differentiated prevention and intervention strategies for stalkers to prevent recidivism and escalation to other violent crimes, given their heterogeneity. It is particularly important to focus interventions on subgroups closely associated with stalking violence risk factors. Histories of substance use, alcohol use, and co-occurring substance and alcohol use, along with psychological distress identified by police, were found to predict moderated violence, with psychological distress additionally predicting severe violence (Landwehr et al., 2024). These findings suggest that clinical interventions such as treatment are especially warranted for the Aggression-destruction subgroup among the five-identified subgroups.

In addition, establishing stalking classification system for police investigation could be valuable, especially considering that many stalking cases in South Korea escalate to murder before any conviction takes place. Such a system could assist police in developing appropriate risk management strategies alongside investigative approaches.

While stalking is typically addressed through legal means such as protection orders and stalker incarceration, legal sanctions alone may not always effectively prevent stalking. Psychological interventions are crucial to addressing the underlying issues (MacKenzie & James, 2011; Racine & Billick, 2014). Although most stalkers do not have a diagnosed mental disorder, a subset may exhibit conditions such as erotomanic Rationalizing Non-compliance (McEwan et al., 2017; McGuire & Wraith, 2000). Additionally, even stalkers without diagnosed mental disorders often display problematic personality traits, such as anger and jealousy following romantic breakups (Dye & Davis, 2003; Manunza & Pintor, 2018; McEwan et al., 2017). The type of psychological intervention required varies depending on the stalker’s mental health. Stalkers with psychotic symptoms may benefit from pharmacotherapy, while those without such symptoms may require psychotherapy. Accurate risk assessment and identification of specific psychological deficits, needs, and responsivity factors are essential for tailored interventions to different stalker subgroups (MacKenzie & James, 2011). A framework for collaboration between police and clinicians should be established to facilitate this process. For instance, police could initially assess and screen problematic stalkers using tools like the UEMC, while clinicians could then reevaluate these individuals and provide specialized services as needed. Therefore, further research is required to explore stalking typology and appropriate interventions for each type.