Chronic Involvement with Canadian Policing Services Beyond Criminal Offending

Chronic Involvement with Canadian Policing Services Beyond Criminal Offending | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Chronic Involvement with Canadian Policing Services Beyond Criminal Offending Allie Wall, Lisa Heslop, Katreena Scott This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7829955/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Prior research has extensively discussed the issue of chronic offending, where a small group of individuals are repeatedly charged by police and are involved in large numbers of criminal offences. Considerably less research explores individuals who are chronically involved with the police for non-criminal reasons. Using police records data, the current study describes the chronic police involvement for both criminal and non-criminal police involved populations. Using a prospective longitudinal design, a baseline sample of 31,755 individuals who were involved with police in 2015, were followed through the police record until 2020. Chronic offenders made up 3.3% of the overall police involved population and were involved in close to one third of all police interactions and over half of all criminal offences. A second group, chronic non-offenders, were also shown to be chronically involved with policing services, but for non-criminal matters. These individuals made up 3.5% of the overall police involved population and were involved in approximately ten percent of all police interactions. Both the chronic offending and chronic non-offending groups differed on measures of demographics and non-criminal police involvement. Policy and practice implications surrounding the use of police data for improved service coordination and prevention insights are discussed. chronic offending police domestic disturbance police involvement Figures Figure 1 Introduction A well-known finding in the criminological literature is that a small group of chronic offending individuals are repeatedly charged by police and are involved in most reported crime (Wolfgang, Figlio, & Sellin, 1972; Carrington, Matarazzo, & De Souza, 2005; Zara & Farrington, 2019). This phenomenon, in which a small group of individuals are disproportionately responsible for a population-based outcome isn’t unique to criminology. The chronic offending outcome mirrors a phenomenon that is more globally known as the Pareto principle, which has been observed in outcomes across a variety of fields including medicine and economics (Corsaro, 2018, p. 251). Criminological research has mostly explored the Pareto principle in relation to criminal offending, however, some evidence suggests that the phenomenon may exist amongst other quantitative policing measures, including victimization frequency (Dudfield, Angel, Sherman, & Torrence, 2017) and police interaction frequency more broadly (Author r.42, 2024). Extending the examination of the Pareto principle in policing research to include these other measures may help to better understand individuals who become chronically involved with the police for criminal and non-criminal incidents. Moreover, research is also needed to understand whether secondary police data can be used to help inform the development and implementation of primary prevention policies and interventions. In other words, can primary prevention insights and potential targets be gleaned by assessing individuals’ police involvement across time? By shifting our focus (and unit of analysis) from ‘criminal offending’ to ‘police interactions’ it becomes possible to observe the full scope of police involvement for individuals, as well as the potential missed service opportunities for those who become repeatedly or chronically involved. Using official police records data from Ontario, Canada, the current study describes the chronic police involvement for both criminal and non-criminal police-involved populations. The authors explore the extent to which chronic offenders and chronic non-offenders become involved with police for non-criminal concerns relating to (1) informally resolved complaints, (2) individual safety concerns (e.g., mental health/health), (3) intimate partner and family conflict (e.g., non-criminal domestic disturbances), and (4) concerns relating to community safety and order (e.g., community disturbances, by-law infractions, disputes). A series of bi-variate and latent class analyses explores differences between chronic offenders and chronic non-offenders on demographics and non-criminal police involvement patterns. Criminological research on chronic offending Chronic offenders are individuals who have a high frequency of criminal offences and are involved in most reported crimes (Farrington & West, 1993; Wolfgang et al., 1972). The concept of chronic offending was first popularized by a Philadelphia-based birth cohort study on adolescent delinquency where Wolfgang and colleagues (1972) observed that a small proportion of individuals (18% of offenders; 6% of the cohort) were involved in over half of all offences in the study. These results have been replicated, again in Philadelphia (Tracy, Wolfgang, & Figlio, 1990), as well as in multiple other jurisdictions including Cambridge, England (Farrington & West, 1993; Whitten, McGee, Homel, Farrington, & Ttofi, 2019), and across numerous provinces in Canada (Carrington et al., 2005; Day et al., 2007; Day et al., 2012; Ibrahim, 2019). Across studies, chronic offenders make up between 3 and 27% of the criminal justice population and are responsible for 52 to 89% of all reported crime (Day et al., 2012; Ibrahim, 2019; Whitten et al., 2019). Prior research has identified a host of risk factors from multiple life domains (individual, family, peer, school, employment, and environmental) that are predictive of chronic offending. These risk factors are understood to be developmental in nature, in that they ‘exert their influence at different stages of development’ (Day et al., 2012, p. 384) and may negatively impact normative developmental processes that traditionally protect individuals against the development of anti-social tendencies (Howe & Covell, 2008). One complication within the chronic offending literature is that many of the risk factors that are predictive of chronic offending overlap with non-chronic offending. Some of the most predominant risk factors that are associated with both general and chronic offending include low socio-economic status, disruptions in family relationships, an adverse family environment, childhood maltreatment, anxious-depressed caregivers, as well as adolescent dishonesty, low school-attainment, hyperactivity, risk taking behaviors, and heavy alcohol use during later adolescence (Farrington & Ttofi, 2012; Whitten et al., 2019). Research into the impact of adverse childhood experiences, such as those listed above, further contribute that each additional adverse childhood experience increases the risk of serious, violent, and chronic offending, with Fox and colleagues (2015) estimating that each additional childhood adversity increases risk by more than 35%. Childhood and adolescent adversities predictive of chronic offending are not just predictive of offending outcomes though; they are also predictive of several other adverse adult outcomes including higher rates of mental illness, greater problems developing and maintaining relationships and poorer physical health (Kalmakis & Chandler, 2015). Adverse childhood experiences are also associated with missed opportunities in the realms of education, prosocial relationships, and employment, resulting in many individuals failing to achieve their full developmental, intellectual, social, and economic potential (Haczkewicz, Shahid, Finnegan, Monnin, Cameron, & Gallant, 2024). Considering this literature, Zara and Farrington (2016, 2019) suggest that offending behavior amongst chronic offenders may be a minor feature of the larger set of problematic outcomes associated with these risks including psych-social maladjustment, social rejection, missed opportunities, and unsuccessful lifestyles (Zara & Farrington, 2016, 2019). Although the multifinality of predictors of chronic offending is well known and accepted, it has seldom been examined within data on police interactions. Applied to an examination of police involvement, it yields questions about the extent and nature of contacts between police and chronic offenders for reasons that may be more closely related to problems with relationships, mental health, and substance use, as opposed to criminal behaviours. Non-criminal police interactions: A meaningful analytical unit to better understand chronic police involvement? In Canada, the context for the current study, individuals can become involved with the police for a wide variety of non-criminal concerns. Canadian policing services have broadly specified mandates and spend a large proportion of their resources responding to non-criminal service calls relating to individual safety (i.e., mental health, welfare checks, missing persons), intimate partner or family conflicts (e.g., domestic disturbances), and concerns of community safety and order (i.e., community disturbances, disputes, by-laws) (Ellingwood, 2015; Huey & Ferguson, 2023; Huey, Schulenberg, & Koziarski, 2022; Iacobucci, 2014). In 2014, a Canadian federal government House of Commons Committee on Public Safety and National Security reviewed the economics of policing across Canada and found that increases in service calls related to social disorder and mental health were among the primary drivers of costs for modern-day Canadian policing (Standing Committee on Public Safety and National Security, 2014). In a later Public Safety Canada report, it was noted that non-criminal concerns such as compassion to locate (i.e., welfare checks) and intimate partner or family conflicts were amongst the most frequent calls for some police agencies in Canada (Ellingwood, 2015). A large body of research has also addressed the increasing rates of police involvement in mental health emergencies (Huey et al., 2022; Pepler & Barber, 2021). It is estimated that Canadian police services can receive upwards of 20,000 mental health-related calls for service each year, with approximately 11 to 31% of all service calls involving a person with mental illness (Koziarski, Ferguson, & Huey, 2022; Wilson-Bates, 2008). Despite the amount of time and resources dedicated to non-criminal policing duties, until recently, police responses to non-criminal concerns were often excluded from Canadian police performance indicators, which traditionally focused on crimes rates and crime severity (Mazowita & Rotenberg, 2019). Such recognition has led to the development, in Canada, of performance indicators for police that better reflect the complexities of contemporary policing outside of strictly law enforcement activities (Mazowita et al., 2019). Specifically, an updated Canadian Police Performance Metrics Framework (CPPMF) developed by Statistics Canada, Public Safety Canada, and the Canadian Association for Chiefs of Police, captures police performance across a wider range of interactions with the public. Similar developments are needed in research. There is currently limited research available on police interactions with individuals for non-criminal concerns. Research data on non-criminal police interactions can be difficult to obtain for academic researchers because, unlike data on criminal charges, this data is not centrally collected and made available. Instead, this data must be shared by each individual Canadian police service. Such sharing requires trusting relationships and comprehensive research agreements between academic researchers and policing services to ensure the data will be used in ways that advance the shared goals of police services and researchers (Huey & Ricciardelli, 2016). This small body of research describing the full range of police interactions has tended to focus on populations living with severe mental illnesses. Such studies have found that severe mental health symptoms, dual diagnoses, family concerns of self-harm, precarious housing, and access to weapons are significant predictors of repeated police contact for persons with mental illness (Akins, Burkhardt, & Lanfear, 2016; Kouyoumdjian et al., 2019; Olmstead, Hoffman, Brown, & Hirdes, 2022; Hoch, Hartford, Heslop, & Stitt, 2009). Most of this research, however, still relies on operationalization of police contact by measuring arrests, charges, or mental health custody events (i.e., types of interactions between police and public that is centrally collected and reported on). A broader lens could be useful for both chronically offending individuals, and other individuals frequently involved with police. Specifically, expanding our unit of analysis from ‘criminal offending’ to ‘police interactions’ may be helpful in the evaluation of police responses to non-criminal service calls, but more importantly, it may also be helpful in developing more evidence-based responses that can better support individuals, increase social service coordination, while also decreasing and preventing chronic offending and chronic police involvement. Current study The current study explores chronic police involvement among offending and non-offending populations. Given that criminality is one of many ways in which adversity and maladjustment presents within the lives of chronic offenders (Zara & Farrington, 2016), it is possible criminally charged individuals are involved with police in a much broader capacity. This research will also explore whether the Pereto principle can be observed within a measure of non-criminal police interaction frequency. Using official police records data and a five-year prospective longitudinal research design, the current paper set out to explore chronic police involvement, for both criminal and non-criminal police-involved populations. Two primary research questions have guided this work: To what are extent are chronic offenders involved in non-criminal police interactions? And is there a population of people who are chronically involved with police for purely non-criminal reasons? Do chronic offenders and chronic non-offenders differ on measures of demographics and the extent and patterns of non-criminal police involvement? Methods Data source, research design and sampling In this study we used an Ontario, Canada municipal police service administrative dataset from 2015 to 2020. Data was de-identified by the police agency, but included some limited demographic information, and the role each person played within the police interaction (i.e., subject, suspect, accused, complainant, victim, and witness). The dataset included multiple person and interaction-level ID variables, allowing the researchers to track individuals’ police interactions across time. Using a prospective longitudinal design, individuals were followed through the administrative police dataset across a five-year period. Our final study sample included individuals who had their baseline police interaction during the year 2015 (n = 31,772), along with all their police interactions that occurred within five years after the baseline interaction date (n = 213,351). Approximately 60% (n = 18,999) of the sample was male-identified and the mean age was 36.5 ( sd = 17). Measures Demographics. Limited demographics were included with the dataset, including binary gender identity and date of birth, which was used to create the baseline age variable. The dataset was missing demographic information pertaining to race, ethnicity, sexuality, non-binary gender identities, and citizenship/immigration status. Police interaction types and frequencies. Referencing the Statistics Canada’s uniform crime reporting (UCR) manual (Canadian Centre for Justice Statistics, 2019), all interactions included in the study were first organized into three broad categories, representing: non-criminal interactions, criminal interactions and non-criminal provincial and municipal tickets 1 (see table one for categories and overall frequencies). Using a combination of the UCR interaction description and the person role variable, non-criminal police interactions were organized into six additional categories, representing interactions relating to: (1) individual safety concerns, (2) intimate partner and family conflict (where there were no charges), (3) community safety and order concerns, (4) complaints (i.e., interactions informally resolved by responding officer) 2 ; (5) contacts where the person was identified as a victim and (6) contacts where the person was identified as a witness. Criminal offence interactions were further organized into three additional categories representing: (7) crimes against property; (8) crimes against persons; and (9) administrative, societal, and ‘other’ offences (e.g., breach of conditions, failure to attend, traffic offences). Most interactions that resulted in a ticket were treated as non-criminal offences and were coded as (10) non-criminal tickets, with more serious traffic offences, including careless or dangerous driving and leaving the scene of an accident being coded as a criminal offence in the ‘administrative, societal & other offences’ category. Three continuous variables were then constructed counting the number of criminal, non-criminal, and total interactions for each person across the five-year study period. Chronic Police Involvement. There is variation in the literature on methods used to identify chronic offenders, or in our case, those individuals with chronic police interactions. Some authors have used trajectory-based modelling to identify sub-groups of chronic offenders (Day et al., 2012; Piquero, 2008); while others have followed the lead of Wolfgang and colleagues (1972) and have used an offence frequency cutoff of five-plus criminal offences to define chronic offending (Ibrahim, 2019; Whitten et al., 2019; Wolfgang et al., 1972). There has been some discussion that addresses the arbitrary nature of the five-plus offence cutoff (Blumstein, Farrington, & Moitra, 1985; Piquero, Farrington, & Blumstein, 2007; Zara & Farrington, 2016); and criminal trajectory research has found evidence of multiple chronic offending sub-groups, with meaningful differences between low- and high-rate chronic offenders (Day et al., 2012; Nagin & Odgers, 2010; Piquero, Farrington, & Blumstein, 2007). Based on this variation, some recent studies have used a frequency cut-off of ten-plus criminal offences to capture chronic offending (Zara & Farrington, 2016). Following Zara and Farrington (2016), the current study used a ten plus cut-off for coding. Individuals who were arrested and/or charged with ten or more criminal offences during the five-year study period were identified as ‘chronic offenders’. Chronic non-offenders were identified using the same frequency cut-off, applied to non-criminal police interactions. Those who were involved in ten-plus non-criminal police interactions (including provincial and municipal tickets) and had no recorded arrested and/or charges during the study period were identified as ‘chronic non-offenders’. Table 1 About Here Table 1 Police Interaction Category Descriptions Interaction Categories Category Descriptions Total Interactions (n = 213,351) Non-Criminal ‘Subject’ Interactions Individual Safety Concerns Mental health, missing persons, check welfare, request for an ambulance 13,265 Intimate Partner and Family Conflict Non-criminal domestic disturbances, involving conflict between intimate partners and/or family members 46,032 Community Safety & Order Community trouble/disturbances, by-law infractions, neighbour disputes, police-initiated interactions 23,463 Complaints Interactions informally resolved by police officers that did not result in a formal police report or intervention. 59,554 ‘Victim’ and ‘Witness’ Interactions Victimization Interactions Recorded as a victim in a police interaction 8,054 Witness Interactions Recorded as a witness in a police interaction 12,308 Criminal ‘Arrested’ and/or ‘Charged’ Interactions Crimes Against Property Fraud, property damage, theft, possession of stolen property 7,959 Crimes Against Persons Threats, assaults, robbery, homicide 6,565 Administrative, Societal, & Other Offences Breach of court orders, traffic offences, drug and alcohol offences, gambling, firearm storage and licensing offences, sex work, immigration 21,756 Provincial & Municipal (non-criminal code) Tickets Non-Criminal Tickets Provincial and municipal by-law offences such as excessive noise, trespassing, animal control, environmental protection, occupational health and safety 14,395 Analyses Univariate analyses were used to describe the police interactions for chronic offending and non-offending groups. Bivariate analyses, including independent t-tests and chi-square tests of independence, were used to explore differences between chronic offending and non-offending groups on demographic and non-criminal police involvement. Simultaneous latent class analyses were used to explore non-criminal police involvement patterns across each study group. Model selection for the latent class models considered both global and relative model fit indices (Collins & Lanza, 2009; Nylund, Asparouhov, & Muthén, 2007). The likelihood ratio chi-square, the Akaike information criteria (AIC, Akaike, 1987), the Bayesian information criteria (BIC; Nylund, et al., 2007; Kuha, 2004), the sample-sized adjusted BIC (aBIC, Sclove, 1987; Nylund et al., 2007), the Lo, Mendell, and Rubin likelihood ratio test (Lo, Mendell, & Rubin, 2001; Nylund et al., 2007), the bootstrap likelihood ratio test (McLachlan & Peel, 2000; Nylund et al., 2007), as well as the precision of classification (entropy) (Muthén, 2004) were evaluated to determine the best fitting model. In addition to the model fit statistics, the interpretability of each model was evaluated based the proportion of individuals in each class (Collins et al., 2009). Results Chronic involvement with police for chronic offenders and chronic non-offenders Using the criteria of ten-plus criminal arrests or charges within a five-year period, we identified a chronic offending subgroup consisting of 3.3% (n = 1,061) of the overall police involved sample included in the dataset (n = 31,772), and 14% of all individuals who were arrested or charged during the study period (n = 7,493). Although this group made up less than 5% of the overall sample, they were shown to be involved in approximately 58% (n = 21,086) of all criminal interactions (n = 36,280), 26% (n = 46,961) of all non-criminal interactions (n = 177,071) and 32% (n = 68,047) of all police interactions (n = 213,351). Those in the chronic offending group had on average 19.8 criminal offences, and 64 police interactions across the five-year study period. Of note, over two-thirds (69%) of police interactions involving chronic offenders were non-criminal in nature, with non-criminal complaints being the largest category, accounting for 37.6% (n = 25,571) of all interactions for chronic offenders. The next most frequent category of interactions for this group was administrative offences, accounting for 19% of their police involvement, followed by tickets (11%), crimes against property (8.3%), and intimate partner and/or family disturbances (8.3%). Approximately 3.5% of interactions (n = 2,412) for the chronic offending group involved crimes against persons. The chronic offending group was also involved in a small proportion of victimization and witness cases, making up 1% and 0.2% of cases respectively. Using the cut-off of 10 or more police interactions, our analyses revealed a second chronically involved group that was of equal size to the chronic offender group, but whose full police involvement was non-criminal in nature. The chronic non-offending group made up 3.5% (n = 1,097) of the sample and were involved in 11.8% of non-criminal police interactions and 9.8% (n = 20,946) of all police interactions included in the study. The chronic non-offending group were most often involved in intimate partner and family disturbance cases, making up 28.7% of all their police interactions. Being a witness was the second largest interaction category for the chronic non-offending group, making up approximately 20% of their interactions. Non-criminal complaints (18%), individual safety concerns (15.5%), and community safety and order (10.4%) were the next most frequent interactions for this group, followed by being a victim (4.3%) and tickets (2.5%). Differences between chronic offenders and chronic non-offenders on demographics and measures of non-criminal police involvement Groups differences were observed on both demographic characteristics and non-criminal police involvement between the chronic offending and chronic non-offending groups (table two). Women made up 53% of the chronic non-offending group but only 22% of the chronic offending group (χ 2 = 226.6, p < .001). The chronic non-offending group was also shown to be slightly older ( = 33.3), compared to the chronic offending group ( = 30.5, p < .001). Regarding police interaction frequency, the chronic offending group was shown to have significantly more non-criminal police interactions ( = 44.2) across the five-year period, compared to the chronic non-offending group ( = 19, p < .001), which is a mostly unsurprising result given the difference in how these groups were operationalized (i.e., 10 or more criminal offences versus 10 or more interactions). When looking at the different types of non-criminal police interactions across groups, it was found that the chronic non-offending group had greater involvement with police due to concerns of individual safety (χ 2 = 2.5, p < .001), intimate partner and family conflict (χ 2 = 2.9, p < .001), victimization cases (χ 2 = 549, p < .001), and witness cases (χ 2 = 9.6, p < .001). The chronic offending group was shown to have greater involvement in cases relating to community safety and order (χ 2 = 19, p < .001), complaints (χ 2 = 7.9, p < .001), and tickets (χ 2 = 2.5, p < .001). Table 2 About Here Table 2 Group Comparisons on Demographics and Non-Criminal Police Interaction Variables Variables Chronic Non-Offenders Chronic Offenders t/ χ 2 p-value Demographics n = 1,097 n = 1,061 Gender (% women) 586 (53%) 233 (22%) 226.6 < .001 Age at Index ( sd ) 33.3 (0.46) 30.5 (0.28) 5.0 < .001 Non-Criminal ‘Subject’ Police Interactions n = 20,946 n = 46,961 Number of Non-criminal Interactions ( sd ) 19 (19) 44.2 (69) 11.6 < .001 Individual Safety Concerns (%) 3,244 (15.5%) 2,024 (4.3%) 2.5 < .001 Intimate Partner & Family Conflict (%) 6,023 (28.7%) 5,637 (12%) 2.9 < .001 Community Safety & Order (%) 2,182 (10.4) 5,430 (11.5%) 19 < .001 Complaints (%) 3,753 (18%) 25,571 (54.4%) 7.9 < .001 ‘Victim’ and ‘Witness’ Police Interactions Victimization Interactions (%) 915 (4.3%) 670 (1.4%) 549 < .001 Witness Interactions (%) 4,312 (20.6%) 165 (0.3%) 9.6 < .001 Tickets Provincial/Municipal Tickets (%) 517 (2.5%) 7,464 (16%) 2.5 < .001 Patterns of non-criminal police involvement for chronic offenders and chronic non-offenders. Simultaneous LCA models for the chronic non-offenders and chronic offenders were used to examine patterns of police interaction over time. Results for the two LCA models were similar in terms of the number of latent classes that emerged from the data, with both groups displaying a two-class model solution. Model selection was easier for the chronic offending group, as each of the fit indices provided clear evidence for a two-class model (table three). For the chronic non-offenders, the likelihood ratio chi-square, a statistical measure that describes whether the model can be generalized to the population, as well as one of the relative fit statistics, the AIC, suggested a four-class model. However, the BIC, the sample-size adjusted BIC, and the interpretability of the models pointed towards a two-class model. The researchers chose the more parsimonious and interpretable solution and thus decided that the two-class model was best fitting for the chronic non-offender group (table three). Overall, the simultaneous LCA provided evidence for partial measurement equivalence across the two groups, as both groups appeared relatively comparable in terms of the number of latent classes and class size, however, there were clear differences on item-response probabilities across the two groups (figure one). Table 3 About Here Table 3 Simultaneous LCA global and relative fit statistics across study groups Class G 2 p -value AIC BIC aBIC Entropy Smallest Class Size (%) LMR LRT p- value BLRT p- value Chronic Offending Group (n = 1,061) 1 83.0 < .001 5085.7 5110.6 5094.7 - - - - 2 11.3 0.94 5025.9 5080.5 5045.6 0.51 252 (24%) < .001 < .001 3 7.7 0.90 5034.4 5118.8 5064.8 0.88 234 (22%) 0.06 0.42 4 5.1 0.74 5043.7 5158.0 5084.9 0.66 55 (5%) 0.50 0.66 5 3.5 0.17 5054.1 5198.2 5106.1 0.98 25 (2%) 0.74 1.00 6 - - 5064.1 5237.9 5126.8 0.77 5 (< 1%) 0.50 1.00 Chronic Non-Offending Group (n = 1,097) 1 253.2 < .001 6235.71 6260.71 6244.83 - - - - 2 55.0 < .001 6050.46 6105.46 6070.52 0.82 139 (12%) < .001 < .001 3 33.3 0.003 6039.72 6070.73 6070.73 0.85 49 (4%) 0.007 0.013 4 11.2 0.19 6029.67 6144.68 6071.63 0.84 59 (5%) 0.045 0.013 5 1.5 0.46 6032.05 6084.95 6084.95 0.76 15 (1%) 0.350 0.500 6 - - 6042.64 6106.50 6106.48 0.77 17 (1.6%) 0.308 1.00 Across both groups, the majority of individuals (76-87%) were categorized into the first class, representing a ‘high service needs’ pattern of non-criminal police involvement. This first class was characterized by a moderate to high-level of involvement with police across each of the five police interaction categories. This first class appeared quite similar across the two groups, with slight differences on class prevalences and item-response probabilities for individual safety, community safety and order, complaints, and tickets interactions (figure one). More chronic non-offenders (87%) were categorized into this high-needs sub-group, compared to chronic offenders (76.5%), however, the chronic offenders presented with higher probabilities of endorsing each individual variable. According to these results, the majority of chronic non-offenders and offenders present to police with complex needs relating to individual safety, relational safety, and community safety. The second class to emerge from the simultaneous LCA displayed both similarities and differences across the two chronically involved groups. The second class appeared relatively similar across groups in terms of class size, making up between 12% (for chronic non-offenders) and 23% (for chronic offenders) of group members. Across groups, individuals categorized in the second class had a low probability (0-.15) of presenting to police due to their individual safety, and a moderate probability (.50-.64) of interacting with police due to an intimate partner or family dispute case. For the chronic non-offenders, the second class was further characterized by a low probability (0.04) for community safety police involvement, a slightly higher probability (.15) of being involved in a non-criminal complaint, and a low probability (0.06) of being involved in an interaction that resulted in a ticket. For these individuals, intimate partner and family conflict appears to be the primary presenting concern. Although this second class makes up a smaller proportion of the chronic non-offending group (12.6%), they were also shown to be involved in significantly more non-criminal police interactions, compared to the first ‘high needs’ class that emerged for the chronic non-offenders (t (1095) = 3.6, p < .001) (figure one). The second class of chronic offenders displayed a more complex picture compared to the chronic non-offender, with these individuals having a moderate to high probability (.65) of being involved in a community safety concern, a high probability of being involved in a non-criminal complaint (.99), and a low-moderate probability (.32) of being ticketed. These individuals appear to have multiple presenting concerns, both with immediate intimate partner and family relationships, as well as within the community, and thus may require additional supports and resources, compared to the chronic non-offenders. Discussion The current study explored the non-criminal police interactions for both chronic offending and non-offending police involved persons across a five-year period in Ontario, Canada. In doing so, we confirmed the existence of the Pereto principle across multiple criminal justice measures, including criminal offence frequency and non-criminal police interaction frequency. As expected, we found that a small proportion of the police involved population were chronically involved in criminal offences. These individuals made up 3.3% of the overall police involved population, 14% of the offending population, and were shown to be involved in close to a third of all police interactions (32%) and over half of all criminal offences (58%). A second group that was of equal size to the chronic offenders were shown to be chronically involved with policing services, but for non-criminal concerns. These individuals made up 3.5% of the overall police involved population and were involved in ten-plus non-criminal police interactions, making up 9.8% of all police interactions. Both the chronic offending and chronic non-offending groups differed on age and gender, with chronic non-offenders shown to be slightly older and more likely to identify as female. The groups also differed on measures of non-criminal police interactions. Chronic non-offenders were most often involved with police due to issues relating to intimate partner and family conflict, witnessing police reported events, informally resolved complaints, and individual safety concerns. On the other hand, the chronic offender group was most often involved with police due to informally resolved complaints, tickets, intimate partner and family conflict, and concerns of community safety and order. Moreover, the chronic offender group was shown to be more highly involved, accumulating a significantly greater number of non-criminal police interactions, compared to the chronic non-offenders. These group differences on non-criminal police involvement have important implications for public safety policies and interventions, demonstrating that those who are chronically involved with police for non-criminal reasons likely have different, but sometimes overlapping, intervention needs compared to those who become chronically involved for both criminal and non-criminal issues. Future research should consider exploring intervention needs for individuals who become chronically involved with police due to intimate partner and family conflict, community complaints, or witnessing crime or other police reported events. In some regards, these group differences weren’t overly surprising, considering we used two different types of justice measures to identify two different groups of justice-involved persons, and thus there is likely unique differences between criminal and non-criminal justice-involved populations, as evidenced by our findings. For an example, building upon general strain theory of crime (Agnew, 2001; Agnew & Brezina, 2019), it’s possible that chronic offenders (compared to chronic non-offenders) have an elevated risk of developing anti-social outcomes due to their unique constellation of severe adverse experiences and unmet criminogenic needs for services. Additional research is needed to better understand the experiences of adversity and access to services for individuals who become chronically involved with police for both criminal and non-criminal concerns. Results from the latent class analyses revealed that each study group had two distinct subgroups based upon their non-criminal interaction patterns with police. The first class to emerge for both the chronic offenders and non-offenders was quite similar and included a majority of group members (76-87%). This first class displayed a high-needs pattern of non-criminal police interactions, where individuals were moderately to highly involved in police due to individual safety concerns, intimate partner and family conflict, community safety and order concerns, and non-criminal complaints. Differences between the chronic offenders and non-offenders were most prevalent within the second latent class. For the chronic non-offenders, the second class involved 12.6% of group members who were largely involved with police due to intimate partner and family conflict, with a smaller probability of being involved in non-criminal complaints. For the chronic offenders, the second class involved 24% of group members who were involved with police for a variety of concerns, including intimate partner and family conflict, as well as community safety, complaints, and tickets. These results demonstrate that most individuals who become chronically involved with police are of high need for supportive services. Three of the four sub-groups to emerge, including the entirety of the chronic offender group and 87% of the chronic non-offender group, displayed a high-needs pattern of non-criminal police interactions, characterized by a moderate-to-high level probability of interacting with police across multiple types of presenting concerns. Non-criminal police interactions: A meaningful analytical unit to better understand chronic police involvement As demonstrated by our results, non-criminal police interactions make up an important analytical unit for understanding the full scope of police involvement for those who are repeatedly or chronically involved with the police. This comes as no surprise, given that policing services in Canada are meant to respond to a wide variety of non-criminal concerns to protect and promote public safety and order. In addition, prior research has shown that chronic offending samples are typically made up of individuals who have experienced severe adversity, maladjustment, and missed opportunities (Fox et al., 2015; Zara et al., 2016, 2019). Thus, it is likely this population has higher needs for services compared to the general population, and that this unmet need for services may be reflected in higher levels of police interaction. Moreover, the existence of both chronic offending and non-offending groups within the data, and the fact that non-criminal incidents made up the majority of chronic offenders’ contact with police, provides further support for the utility of justice system measures that go beyond criminal offending. Our analyses suggest that both chronic offending and non-offending groups are in frequent contact with police due to non-criminal complaints, intimate partner and family conflict, community safety and order concerns, and individual health and safety all of which may serve as potential therapeutic intervention targets to address the issue of chronic police involvement. By attending to the full police interaction record we can temporarily shift our analytical perspective from ‘individual problem behavior’ (i.e., criminal offending) to ‘system utilization patterns’. This can allow researchers, practitioners, and policy makers to zoom out and observe a more complete picture of how and why individuals become repeatedly involved in the justice system. This is not to say that criminal offending measures should be replaced, rather, they should be supplemented with other available information. Secondly, measures of non-criminal police interactions may be useful in evaluating policing services, including the deployment of policing resources, and the efficacy of reliance on a police response across different types of calls for service. This data may support public health and safety goals to develop evidence-based policing responses, and more effective coordination between policing, community services, and other public health and safety services (i.e., health, mental health, child welfare) (Pepler & Macnamara., 2024). The numbers presented in our results are helpful and relevant. In the community in which this police service is located, there are approximately 1000 individuals who were chronically involved with police for non-criminal reasons, and an additional 1000 individuals who were involved for both non-criminal and criminal reasons. Together, these groups are responsible for almost 90,000 interactions with police over five years, or approximately 18,000 interactions per year. The study adds to the voices of prior researchers and advocates (Ellingwood, 2015, Pepler et al., 2024) in concluding that the lives and costs associated with this level of police service demands robust community-police collaborations to meet the needs of this group of individuals. Limitations First, it’s important to acknowledge the limitations of using police UCR data. This type of data has limited information on the people involved with the police, the precipitating events that resulted in their involvement, and the outcomes of the interaction. This study was missing information on how exactly police cleared each interaction and whether they made a service referral during the interaction. We were also missing important information pertaining to individuals’ demographics, including non-binary gender identity, sexuality, race, and ethnicity. It is also important to acknowledge that there is a lack of standardization of police data collection and sharing practices across organizations within Canada (Huey, Ferguson, & Vaughan, 2021). This greatly reduces the ability to compare results across policing jurisdictions. Finally, it’s important to acknowledge the limitations of the chronic police involvement measurements that were applied in this study. Previous research has shown that ten-plus criminal offences is an adequate measure for chronic offending, however, no such research exists for chronic non-offending, and thus there may be alternative measures of chronic non-offending police involvement that should be considered in future research. Moreover, our results demonstrated that chronic offending and non-offending groups differed on both demographics and police involvement measures at the bivariate level. These comparisons may have been driven by the fact that we used different types of justice data measurements to identify two different groups of justice-involved persons. Additional research on chronic non-offending populations, and how they might differ from offending populations, is needed to better understand these two unique justice-involved populations and their needs for public services. Conclusion Prior research has extensively covered the topic of chronic offending; however, little is known about how chronic offending populations interact with police beyond their involvement in crime. Prior research is also lacking on the extent to which non-offending populations become chronically involved with the police for non-criminal matters. Future research should continue exploring chronic police involvement for offending and non-offending police involved populations. Research is needed to explore childhood and youth involvement with police more broadly beyond their involvement in delinquency and crime, and whether childhood and youth populations are becoming chronically involved with police. Future research should also explore chronic police involvement for specific populations, including individuals with severe health and mental health disorders. It’s important to disentangle police involvement for these populations and to make distinctions between police involvement for help seeking versus police involvement for delinquency and crime. Finally, future research should continue to advocate for increased public service data infrastructure that can better connect and integrate data from across public services, supporting our understanding of public service utilization patterns, and how to better coordinate services to promote public health and safety. Declarations Author Contribution A.W. wrote the main manuscript text with the support and supervision from K.S. and L.H. All authors reviewed and made edits to the final manuscript. The authors report there are no competing interests to declare . Funding declaration: No funding was received to support this work. References Agnew, R. (2001). Building on the foundation of general strain theory: Specifying the types of strain most likely to lead to crime and delinquency. Journal of research in crime and delinquency , 38 (4), 319-361. Agnew, R., Brezina, T. (2019). General Strain Theory. In: Krohn, M., Hendrix, N., Penly Hall, G., Lizotte, A. (eds) Handbook on Crime and Deviance. Handbooks of Sociology and Social Research. Springer, Cham. https://doi.org/10.1007/978-3-030-20779-3_8 Akaike, H. (1987). Factor analysis and AIC. Psychometrika , 52 , 317-332. Akins, S., Burkhardt, B. C., & Lanfear, C. (2016). Law enforcement response to ‘frequent fliers’ an examination of high-frequency contacts between police and justice-involved persons with mental illness. Criminal justice policy review , 27 (1), 97-114. Blumstein, A., Farrington, D. P., & Moitra, S. (1985). Delinquency careers: Innocents, desisters, and persisters. Crime and justice , 6 , 187-219. Canadian Centre for Justice Statistics (2019). Uniform Crime Reporting Survey (UCR) Manual. Statistics Canada. Carrington, P. J., Matarazzo, A., & DeSouza, P. (2005). Court careers of a Canadian birth cohort . Ottawa, ON: Statistics Canada, Canadian Centre for Justice Statistics. Collins, L. M., & Lanza, S. T. (2009). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences . John Wiley & Sons. Corsaro, N. (2018). More than lightning in a bottle and far from ready-made. Criminology & Pub. Policy , 17 , 251. Day, D. M., Bevc, I., Duchesne, T., Rosenthal, J. S., Sun, Y., Theodor, F., ... & Geguzinskis, D. (2007, June). Criminal trajectories from adolescence to adulthood in an Ontario sample of offenders. Canadian Psychological Association/North American Correctional and Criminal Justice Psychology Conference (Ottawa, Ontario, 7th June 2007) . Day, D. M., Nielsen, J. D., Ward, A. K., Sun, Y., Rosenthal, J. S., Duchesne, T., ... & Rossman, L. (2012). Long-term follow-up of criminal activity with adjudicated youth in Ontario: Identifying offence trajectories and predictors/correlates of trajectory group membership. Canadian Journal of Criminology and Criminal Justice , 54 (4), 377-413. Dudfield, G., Angel, C., Sherman, L. W., & Torrence, S. (2017). The ‘power curve’ of victim harm: Targeting the distribution of crime harm index values across all victims and repeat victims over 1 year. Cambridge Journal of Evidence-Based Policing , 1 , 38-58. Ellingwood, H. (2016). A better estimation of police costs by offence types . Public Safety Canada. Farrington, D. P., & West, D. J. (1993). Criminal, penal and life histories of chronic offenders: Risk and protective factors and early identification. Criminal behaviour and mental health , 3 (4), 492-523. Farrington, D. P., & Ttofi, M. M. (2012). Protective and promotive factors in the development of offending. In T. Bliesener, A. Beelmann, & M. Stemmler (Eds.), Antisocial behavior and crime: Contributions of developmental and evaluation research to prevention and intervention (pp. 71–88). Hogrefe Publishing. Fox, B. H., Perez, N., Cass, E., Baglivio, M. T., & Epps, N. (2015). Trauma changes everything: Examining the relationship between adverse childhood experiences and serious, violent and chronic juvenile offenders. Child abuse & neglect , 46 , 163-173. Haczkewicz, K. M., Shahid, S., Finnegan, H. A., Moninn, C., Cameron, C. D., & Gallant, N. L. (2024). Adverse childhood experiences (ACEs), resilience, and outcomes in older adulthood: A scoping review. Child Abuse & Neglect , 106864. Hoch, J. S., Hartford, K., Heslop, L., & Stitt, L. (2009). Mental illness and police interactions in a mid-sized Canadian city: What the data do and do not say. Canadian Journal of Community Mental Health , 28 (1), 49-66. Howe, B., & Covell, K. (2008). Children, Families and Violence: Challenges for Children's Rights . Jessica Kingsley Publishers. Huey, L., & Ferguson, L. (2023). ‘Did not return in time for curfew’: A descriptive analysis of homeless missing persons cases. International Criminal Justice Review , 33 (1), 87-101. Huey, L., Ferguson, L., & Vaughan, A. D. (2021). The limits of our knowledge: tracking the size and scope of police involvement with persons with mental illness. Facets , 6 (1), 424-448 Huey, L., & Ricciardelli, R. (2016). From seeds to orchards: Using evidence-based policing to address Canada’s policing research needs. Canadian journal of criminology and criminal justice , 58 (1), 119-131. Huey, L., Schulenberg, J. L., & Koziarski, J. (2022). Policing Mental Health: Public Safety and Crime Prevention in Canada . Springer. Iacobucci, F. (2014). Police Encounters with People in Crisis . Toronto Police Service. Ibrahim, D. (2019). Youth re-contact with the Nova Scotia justice system, 2012/2013 to 2014/2015. Juristat: Canadian Centre for Justice Statistics , 1-18. Kalmakis, K. A., & Chandler, G. E. (2015). Health consequences of adverse childhood experiences: A systematic review. Journal of the American Association of Nurse Practitioners , 27 (8), 457-465. Kouyoumdjian, F. G., Wang, R., Mejia-Lancheros, C., Owusu-Bempah, A., Nisenbaum, R., O’Campo, P., ... & Hwang, S. W. (2019). Interactions between police and persons who experience homelessness and mental illness in Toronto, Canada: findings from a prospective study. The Canadian Journal of Psychiatry , 64 (10), 718-725. Koziarski, J., Ferguson, L., & Huey, L. (2022). Shedding light on the dark figure of police mental health calls for service. Policing: A Journal of Policy and Practice , 16 (4), 696-706. Kuha, J. (2004). AIC and BIC: Comparisons of assumptions and performance. Sociological methods & research , 33 (2), 188-229. Lo, Y., Mendell, N. R., & Rubin, D. B. (2001). Testing the number of components in a normal mixture. Biometrika , 88 (3), 767-778. Mazowita, B., & Rotenberg, C. (2019). The Canadian police performance metrics framework: Standardized indicators for police services in Canada. Juristat: Canadian Centre for Justice Statistics , 1-13. McLachlan, G. J., & Peel, D. (2000). Finite mixture models . John Wiley & Sons. Muthén, B. (2004). Latent variable analysis. The Sage handbook of quantitative methodology for the social sciences , 345 (368), 106-109. Nagin, D. S., & Odgers, C. L. (2010). Group-based trajectory modeling in clinical research. Annual review of clinical psychology , 6 (1), 109-138. Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural equation modeling: A multidisciplinary Journal , 14 (4), 535-569. Olmstead, B., Hoffman, R., Brown, G. P., & Hirdes, J. P. (2022). Using the interRAI brief mental health screener to identify persons with mental disorders having repeat contact with police. International journal of law and psychiatry , 83 , 101816. Pepler, E. F., & Barber, C. G. (2021). Mental health and policing: picking up the pieces in a broken system. In Healthcare Management Forum (Vol. 34, No. 2, pp. 93-99). Sage CA: Los Angeles, CA: SAGE Publications. Pepler, E., & Macnamara, W. D. (2024, March). Our public safety system is a perfect storm. In Healthcare Management Forum (Vol. 37, No. 2, pp. 56-62). Sage CA: Los Angeles, CA: SAGE Publications. Piquero, A. R. (2008). Taking stock of developmental trajectories of criminal activity over the life course. In The long view of crime: A synthesis of longitudinal research (pp. 23-78). New York, NY: Springer New York. Piquero, A. R., Farrington, D. P., & Blumstein, A. (2007). Key issues in criminal career research: New analyses of the Cambridge Study in Delinquent Development . Cambridge University Press. Sclove, S. L. (1987). Application of model-selection criteria to some problems in multivariate analysis. Psychometrika , 52 (3), 333-343. Standing Committee on Public Safety and National Security (2014). Economics of Policing. House of Commons, Canada, 41 st Parliament, Second Session. Tracy, P. E., Wolfgang, M. E., Figlio, R. M. (1990). The Chronic Juvenile Offender. Delinquency Careers in Two Birth Cohorts , 81-97. Author r.42. (2024). Whitten, T., McGee, T. R., Homel, R., Farrington, D. P., & Ttofi, M. (2019). Comparing the criminal careers and childhood risk factors of persistent, chronic, and persistent–chronic offenders. Australian & New Zealand journal of criminology , 52 (2), 151-173. Wilson-Bates, F., & Chu, J. (2008). Lost in transition: How a lack of capacity in the mental health system is failing Vancouver's mentally ill and draining police resources . Vancouver Police Department. Wolfgang, M. E., Figlio, R. M., & Sellin, T. (1972). Delinquency in a birth cohort . University of Chicago Press. Zara, G., & Farrington, D. P. (2016). Chronic offenders and the syndrome of antisociality: Offending is only a minor feature! Irish Probation Journal , 13 , 40-64. Zara, G., & Farrington, D. P. (2019). Unsuccessful lifestyle in middle-aged official and self-reported types of offenders. Journal of Criminal Justice , 64 , 101624. Footnotes Non-criminal ticket offences represent instances where an individual broke a municipal by law (e.g., excessive noise, animal control) or a provincial regulations law (e.g., trespassing, environmental protections, occupational health and safety, traffic violations). These interactions are often not formally documented in a police occurrence report and are missing information regarding the occurrence description and UCR code, therefore, it’s impossible to know what type of circumstances initiated these police interactions, however, we can ascertain that they were of a less serious nature. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7829955","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593969473,"identity":"eed62aba-0663-4ff3-8a51-61f1aa9f2296","order_by":0,"name":"Allie Wall","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYHACxgMPKmzkSNNzIOFMmjGJWhLbDic2EK1cd0bygwMJbGnpG24kH2D4UUOEFrMbaQYHEnhscjfcSEtg7DlGjJbbCUAtEmm5G27nGDAzsBGlJf3DgQSDw+kGYC3/iNKSA7Ql4XACWAtjGzFa7r8pOJBwIM1w5v1nCQd7+4jRcub4xgcf/9nI8505fPDBj29EaEEBB0jVMApGwSgYBaMABwAAhO1AcVPfTRcAAAAASUVORK5CYII=","orcid":"","institution":"Western University","correspondingAuthor":true,"prefix":"","firstName":"Allie","middleName":"","lastName":"Wall","suffix":""},{"id":593969474,"identity":"267d1895-04c9-45e4-a6ec-7d2bca366f5c","order_by":1,"name":"Lisa Heslop","email":"","orcid":"","institution":"Western University","correspondingAuthor":false,"prefix":"","firstName":"Lisa","middleName":"","lastName":"Heslop","suffix":""},{"id":593969475,"identity":"b1beb0e9-aaa0-44c3-8ebf-b2d6066849cc","order_by":2,"name":"Katreena Scott","email":"","orcid":"","institution":"Western University","correspondingAuthor":false,"prefix":"","firstName":"Katreena","middleName":"","lastName":"Scott","suffix":""}],"badges":[],"createdAt":"2025-10-10 20:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7829955/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7829955/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103197354,"identity":"2d6c096d-f54e-4028-8af5-069a6b8b7294","added_by":"auto","created_at":"2026-02-23 04:37:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":164731,"visible":true,"origin":"","legend":"\u003cp\u003ea. Non-criminal police interaction latent class models for chronic non-offenders. Mean (\u003cem\u003e X̄ \u0026nbsp;\u003c/em\u003e) criminal and non-criminal police interaction frequencies are shown for each latent class. (*) indicates that mean comparison between classes were significant (\u003cem\u003ep\u003c/em\u003e\u0026lt; .05).\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eb. Non-criminal police interaction latent class models for chronic offenders. Mean (\u003cem\u003e X̄ \u0026nbsp;\u003c/em\u003e) criminal and non-criminal police interaction frequencies are shown for each latent class. (*) indicates that mean comparison between classes were significant (\u003cem\u003ep\u003c/em\u003e \u0026lt; .05).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7829955/v1/aa81ddb2343e29fef20d3898.png"},{"id":103505937,"identity":"72ec2092-2224-48dc-9bb2-eef4e1f8f65a","added_by":"auto","created_at":"2026-02-26 13:33:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1072693,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7829955/v1/79e1e80a-f284-465e-82d6-4e662aae42f8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Chronic Involvement with Canadian Policing Services Beyond Criminal Offending","fulltext":[{"header":"Introduction","content":"\u003cp\u003eA well-known finding in the criminological literature is that a small group of chronic offending individuals are repeatedly charged by police and are involved in most reported crime (Wolfgang, Figlio, \u0026amp; Sellin, 1972; Carrington, Matarazzo, \u0026amp; De Souza, 2005; Zara \u0026amp; Farrington, 2019). This phenomenon, in which a small group of individuals are disproportionately responsible for a population-based outcome isn\u0026rsquo;t unique to criminology. The chronic offending outcome mirrors a phenomenon that is more globally known as the Pareto principle, which has been observed in outcomes across a variety of fields including medicine and economics (Corsaro, 2018, p. 251). Criminological research has mostly explored the Pareto principle in relation to criminal offending, however, some evidence suggests that the phenomenon may exist amongst other quantitative policing measures, including victimization frequency (Dudfield, Angel, Sherman, \u0026amp; Torrence, 2017) and police interaction frequency more broadly (Author r.42, 2024). Extending the examination of the Pareto principle in policing research to include these other measures may help to better understand individuals who become chronically involved with the police for criminal and non-criminal incidents. Moreover, research is also needed to understand whether secondary police data can be used to help inform the development and implementation of primary prevention policies and interventions. In other words, can primary prevention insights and potential targets be gleaned by assessing individuals\u0026rsquo; police involvement across time? By shifting our focus (and unit of analysis) from \u0026lsquo;criminal offending\u0026rsquo; to \u0026lsquo;police interactions\u0026rsquo; it becomes possible to observe the full scope of police involvement for individuals, as well as the potential missed service opportunities for those who become repeatedly or chronically involved.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsing official police records data from Ontario, Canada, the current study describes the chronic police involvement for both criminal and non-criminal police-involved populations. The authors explore the extent to which chronic offenders and chronic non-offenders become involved with police for non-criminal concerns relating to (1) informally resolved complaints, (2) individual safety concerns (e.g., mental health/health), (3) intimate partner and family conflict (e.g., non-criminal domestic disturbances), and (4) concerns relating to community safety and order (e.g., community disturbances, by-law infractions, disputes). A series of bi-variate and latent class analyses explores differences between chronic offenders and chronic non-offenders on demographics and non-criminal police involvement patterns.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCriminological research on chronic offending\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChronic offenders are individuals who have a high frequency of criminal offences and are involved in most reported crimes (Farrington \u0026amp; West, 1993; Wolfgang et al., 1972). The concept of chronic offending was first popularized by a Philadelphia-based birth cohort study on adolescent delinquency where Wolfgang and colleagues (1972) observed that a small proportion of individuals (18% of offenders; 6% of the cohort) were involved in over half of all offences in the study. These results have been replicated, again in Philadelphia (Tracy, Wolfgang, \u0026amp; Figlio, 1990), as well as in multiple other jurisdictions including Cambridge, England (Farrington \u0026amp; West, 1993; Whitten, McGee, Homel, Farrington, \u0026amp; Ttofi, 2019), and across numerous provinces in Canada (Carrington et al., 2005; Day et al., 2007; Day et al., 2012; Ibrahim, 2019). Across studies, chronic offenders make up between 3 and 27% of the criminal justice population and are responsible for 52 to 89% of all reported crime (Day et al., 2012; Ibrahim, 2019; Whitten et al., 2019).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrior research has identified a host of risk factors from multiple life domains (individual, family, peer, school, employment, and environmental) that are predictive of chronic offending. These risk factors are understood to be developmental in nature, in that they \u0026lsquo;exert their influence at different stages of development\u0026rsquo; (Day et al., 2012, p. 384) and may negatively impact normative developmental processes that traditionally protect individuals against the development of anti-social tendencies (Howe \u0026amp; Covell, 2008). One complication within the chronic offending literature is that many of the risk factors that are predictive of chronic offending overlap with non-chronic offending. Some of the most predominant risk factors that are associated with both general and chronic offending include low socio-economic status, disruptions in family relationships, an adverse family environment, childhood maltreatment, anxious-depressed caregivers, as well as adolescent dishonesty, low school-attainment, hyperactivity, risk taking behaviors, and heavy alcohol use during later adolescence (Farrington \u0026amp; Ttofi, 2012; Whitten et al., 2019). Research into the impact of adverse childhood experiences, such as those listed above, further contribute that each additional adverse childhood experience increases the risk of serious, violent, and chronic offending, with Fox and colleagues (2015) estimating that each additional childhood adversity increases risk by more than 35%. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChildhood and adolescent adversities predictive of chronic offending are not just predictive of offending outcomes though; they are also predictive of several other adverse adult outcomes including higher rates of mental illness, greater problems developing and maintaining relationships and poorer physical health (Kalmakis \u0026amp; Chandler, 2015). Adverse childhood experiences are also associated with missed opportunities in the realms of education, prosocial relationships, and employment, resulting in many individuals failing to achieve their full developmental, intellectual, social, and economic potential (Haczkewicz, Shahid, Finnegan, Monnin, Cameron, \u0026amp; Gallant, 2024). Considering this literature, Zara and Farrington (2016, 2019) suggest that offending behavior amongst chronic offenders may be a minor feature of the larger set of problematic outcomes associated with these risks including psych-social maladjustment, social rejection, missed opportunities, and unsuccessful lifestyles (Zara \u0026amp; Farrington, 2016, 2019). Although the multifinality of predictors of chronic offending is well known and accepted, it has seldom been examined within data on police interactions. Applied to an examination of police involvement, it yields questions about the extent and nature of contacts between police and chronic offenders for reasons that may be more closely related to problems with relationships, mental health, and substance use, as opposed to criminal behaviours.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNon-criminal police interactions: A meaningful analytical unit to better understand chronic police involvement?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn Canada, the context for the current study, individuals can become involved with the police for a wide variety of non-criminal concerns. Canadian policing services have broadly specified mandates and spend a large proportion of their resources responding to non-criminal service calls relating to individual safety (i.e., mental health, welfare checks, missing persons), intimate partner or family conflicts (e.g., domestic disturbances), and concerns of community safety and order (i.e., community disturbances, disputes, by-laws) (Ellingwood, 2015; Huey \u0026amp; Ferguson, 2023; Huey, Schulenberg, \u0026amp; Koziarski, 2022; Iacobucci, 2014). In 2014, a Canadian federal government House of Commons Committee on Public Safety and National Security reviewed the economics of policing across Canada and found that increases in service calls related to social disorder and mental health were among the primary drivers of costs for modern-day Canadian policing (Standing Committee on Public Safety and National Security, 2014). In a later Public Safety Canada report, it was noted that non-criminal concerns such as compassion to locate (i.e., welfare checks) and intimate partner or family conflicts were amongst the most frequent calls for some police agencies in Canada (Ellingwood, 2015). A large body of research has also addressed the increasing rates of police involvement in mental health emergencies (Huey et al., 2022; Pepler \u0026amp; Barber, 2021). It is estimated that Canadian police services can receive upwards of 20,000 mental health-related calls for service each year, with approximately 11 to 31% of all service calls involving a person with mental illness (Koziarski, Ferguson, \u0026amp; Huey, 2022; Wilson-Bates, 2008).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite the amount of time and resources dedicated to non-criminal policing duties, until recently, police responses to non-criminal concerns were often excluded from Canadian police performance indicators, which traditionally focused on crimes rates and crime severity (Mazowita \u0026amp; Rotenberg, 2019). Such recognition has led to the development, in Canada, of performance indicators for police that better reflect the complexities of contemporary policing outside of strictly law enforcement activities (Mazowita et al., 2019). Specifically, an updated Canadian Police Performance Metrics Framework (CPPMF) developed by Statistics Canada, Public Safety Canada, and the Canadian Association for Chiefs of Police, captures police performance across a wider range of interactions with the public.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimilar developments are needed in research. There is currently limited research available on police interactions with individuals for non-criminal concerns. Research data on non-criminal police interactions can be difficult to obtain for academic researchers because, unlike data on criminal charges, this data is not centrally collected and made available. Instead, this data must be shared by each individual Canadian police service. Such sharing requires trusting relationships and comprehensive research agreements between academic researchers and policing services to ensure the data will be used in ways that advance the shared goals of police services and researchers (Huey \u0026amp; Ricciardelli, 2016). This small body of research describing the full range of police interactions has tended to focus on populations living with severe mental illnesses. Such studies have found that severe mental health symptoms, dual diagnoses, family concerns of self-harm, precarious housing, and access to weapons are significant predictors of repeated police contact for persons with mental illness (Akins, Burkhardt, \u0026amp; Lanfear, 2016; Kouyoumdjian et al., 2019; Olmstead, Hoffman, Brown, \u0026amp; Hirdes, 2022; Hoch, Hartford, Heslop, \u0026amp; Stitt, 2009). Most of this research, however, still relies on operationalization of police contact by measuring arrests, charges, or mental health custody events (i.e., types of interactions between police and public that is centrally collected and reported on). A broader lens could be useful for both chronically offending individuals, and other individuals frequently involved with police. Specifically, expanding our unit of analysis from \u0026lsquo;criminal offending\u0026rsquo; to \u0026lsquo;police interactions\u0026rsquo; may be helpful in the evaluation of police responses to non-criminal service calls, but more importantly, it may also be helpful in developing more evidence-based responses that can better support individuals, increase social service coordination, while also decreasing and preventing chronic offending and chronic police involvement.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCurrent study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current study explores chronic police involvement among offending and non-offending populations. Given that criminality is one of many ways in which adversity and maladjustment presents within the lives of chronic offenders (Zara \u0026amp; Farrington, 2016), it is possible criminally charged individuals are involved with police in a much broader capacity. This research will also explore whether the Pereto principle can be observed within a measure of non-criminal police interaction frequency. Using official police records data and a five-year prospective longitudinal research design, the current paper set out to explore chronic police involvement, for both criminal and non-criminal police-involved populations. Two primary research questions have guided this work:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eTo what are extent are chronic offenders involved in non-criminal police interactions? \u0026nbsp;And is there a population of people who are chronically involved with police for purely non-criminal reasons?\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDo chronic offenders and chronic non-offenders differ on measures of demographics and the extent and patterns of non-criminal police involvement?\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData source, research design and sampling\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study we used an Ontario, Canada municipal police service administrative dataset from 2015 to 2020. Data was de-identified by the police agency, but included some limited demographic information, and the role each person played within the police interaction (i.e., subject, suspect, accused, complainant, victim, and witness). The dataset included multiple person and interaction-level ID variables, allowing the researchers to track individuals\u0026rsquo; police interactions across time. Using a prospective longitudinal design, individuals were followed through the administrative police dataset across a five-year period. Our final study sample included individuals who had their baseline police interaction during the year 2015 (n = 31,772), along with all their police interactions that occurred within five years after the baseline interaction date (n = 213,351). Approximately 60% (n = 18,999) of the sample was male-identified and the mean age was 36.5 (\u003cem\u003esd\u003c/em\u003e = 17).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMeasures\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDemographics.\u003c/em\u003e\u0026nbsp;\u003c/strong\u003eLimited demographics were included with the dataset, including binary gender identity and date of birth, which was used to create the baseline age variable. The dataset was missing demographic information pertaining to race, ethnicity, sexuality, non-binary gender identities, and citizenship/immigration status. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePolice interaction types and frequencies.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eReferencing the Statistics Canada\u0026rsquo;s uniform crime reporting (UCR) manual (Canadian Centre for Justice Statistics, 2019), all interactions included in the study were first organized into three broad categories, representing: non-criminal interactions, criminal interactions and non-criminal provincial and municipal tickets\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e1\u003c/sup\u003e (see table one for categories and overall frequencies). Using a combination of the UCR interaction description and the person role variable, non-criminal police interactions were organized into six additional categories, representing interactions relating to: (1) individual safety concerns, (2) intimate partner and family conflict (where there were no charges), (3) community safety and order concerns, (4) complaints (i.e., interactions informally resolved by responding officer)\u003ca href=\"#_ftn2\" name=\"_ftnref2\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e2\u003c/sup\u003e; (5) contacts where the person was identified as a victim and (6) contacts where the person was identified as a witness. Criminal offence interactions were further organized into three additional categories representing: (7) crimes against property; (8) crimes against persons; and (9) administrative, societal, and \u0026lsquo;other\u0026rsquo; offences (e.g., breach of conditions, failure to attend, traffic offences). Most interactions that resulted in a ticket were treated as non-criminal offences and were coded as (10) non-criminal tickets, with more serious traffic offences, including careless or dangerous driving and leaving the scene of an accident being coded as a criminal offence in the \u0026lsquo;administrative, societal \u0026amp; other offences\u0026rsquo; category. Three continuous variables were then constructed counting the number of criminal, non-criminal, and total interactions for each person across the five-year study period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eChronic Police Involvement.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eThere is variation in the literature on methods used to identify chronic offenders, or in our case, those individuals with chronic police interactions. Some authors have used trajectory-based modelling to identify sub-groups of chronic offenders (Day et al., 2012; Piquero, 2008); while others have followed the lead of Wolfgang and colleagues (1972) and have used an offence frequency cutoff of five-plus criminal offences to define chronic offending (Ibrahim, 2019; Whitten et al., 2019; Wolfgang et al., 1972). There has been some discussion that addresses the arbitrary nature of the five-plus offence cutoff (Blumstein, Farrington, \u0026amp; Moitra, 1985; Piquero, Farrington, \u0026amp; Blumstein, 2007; Zara \u0026amp; Farrington, 2016); and criminal trajectory research has found evidence of multiple chronic offending sub-groups, with meaningful differences between low- and high-rate chronic offenders (Day et al., 2012; Nagin \u0026amp; Odgers, 2010; Piquero, Farrington, \u0026amp; Blumstein, 2007). Based on this variation, some recent studies have used a frequency cut-off of ten-plus criminal offences to capture chronic offending (Zara \u0026amp; Farrington, 2016). Following Zara and Farrington (2016), the current study used a ten plus cut-off for coding. Individuals who were arrested and/or charged with ten or more criminal offences during the five-year study period were identified as \u0026lsquo;chronic offenders\u0026rsquo;. Chronic non-offenders were identified using the same frequency cut-off, applied to non-criminal police interactions. Those who were involved in ten-plus non-criminal police interactions (including provincial and municipal tickets) and had no recorded arrested and/or charges during the study period were identified as \u0026lsquo;chronic non-offenders\u0026rsquo;.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1 About Here\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003e\u003cem\u003ePolice Interaction Category Descriptions\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"633\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eInteraction Categories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 326px;\"\u003e\n \u003cp\u003eCategory Descriptions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eTotal Interactions\u003c/p\u003e\n \u003cp\u003e(n = 213,351)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 633px;\"\u003e\n \u003cp\u003eNon-Criminal \u0026lsquo;Subject\u0026rsquo; Interactions\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eIndividual Safety Concerns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 326px;\"\u003e\n \u003cp\u003eMental health, missing persons, check welfare, request for an ambulance\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e13,265\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eIntimate Partner and Family Conflict\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 326px;\"\u003e\n \u003cp\u003eNon-criminal domestic disturbances, involving conflict between intimate partners and/or family members\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e46,032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eCommunity Safety \u0026amp; Order\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 326px;\"\u003e\n \u003cp\u003eCommunity trouble/disturbances, by-law infractions, neighbour disputes, police-initiated interactions\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e23,463\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eComplaints\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 326px;\"\u003e\n \u003cp\u003eInteractions informally resolved by police officers that did not result in a formal police report or intervention.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e59,554\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 633px;\"\u003e\n \u003cp\u003e\u0026lsquo;Victim\u0026rsquo; and \u0026lsquo;Witness\u0026rsquo; Interactions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eVictimization Interactions\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 326px;\"\u003e\n \u003cp\u003eRecorded as a victim in a police interaction\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e8,054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eWitness Interactions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 326px;\"\u003e\n \u003cp\u003eRecorded as a witness in a police interaction\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e12,308\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 633px;\"\u003e\n \u003cp\u003eCriminal \u0026lsquo;Arrested\u0026rsquo; and/or \u0026lsquo;Charged\u0026rsquo; Interactions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eCrimes Against Property\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 326px;\"\u003e\n \u003cp\u003eFraud, property damage, theft, possession of stolen property\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e7,959\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eCrimes Against Persons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 326px;\"\u003e\n \u003cp\u003eThreats, assaults, robbery, homicide\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e6,565\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eAdministrative, Societal, \u0026amp; Other Offences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 326px;\"\u003e\n \u003cp\u003eBreach of court orders, traffic offences, drug and alcohol offences, gambling, firearm storage and licensing offences, sex work, immigration\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e21,756\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 633px;\"\u003e\n \u003cp\u003eProvincial \u0026amp; Municipal (non-criminal code) Tickets\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eNon-Criminal Tickets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 326px;\"\u003e\n \u003cp\u003eProvincial and municipal by-law offences such as excessive noise, trespassing, animal control, environmental protection, occupational health and safety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e14,395\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAnalyses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate analyses were used to describe the police interactions for chronic offending and non-offending groups. Bivariate analyses, including independent t-tests and chi-square tests of independence, were used to explore differences between chronic offending and non-offending groups on demographic and non-criminal police involvement. Simultaneous latent class analyses were used to explore non-criminal police involvement patterns across each study group. Model selection for the latent class models considered both global and relative model fit indices (Collins \u0026amp; Lanza, 2009; Nylund, Asparouhov, \u0026amp; Muth\u0026eacute;n, 2007). The likelihood ratio chi-square, the Akaike information criteria (AIC, Akaike, 1987), the Bayesian information criteria (BIC; Nylund, et al., 2007; Kuha, 2004), the sample-sized adjusted BIC (aBIC, Sclove, 1987; Nylund et al., 2007), the Lo, Mendell, and Rubin likelihood ratio test (Lo, Mendell, \u0026amp; Rubin, 2001; Nylund et al., 2007), the bootstrap likelihood ratio test (McLachlan \u0026amp; Peel, 2000; Nylund et al., 2007), as well as the precision of classification (entropy) (Muth\u0026eacute;n, 2004) were evaluated to determine the best fitting model. In addition to the model fit statistics, the interpretability of each model was evaluated based the proportion of individuals in each class (Collins et al., 2009).\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eChronic involvement with police for chronic offenders and chronic non-offenders\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the criteria of ten-plus criminal arrests or charges within a five-year period, we identified a chronic offending subgroup consisting of 3.3% (n = 1,061) of the overall police involved sample included in the dataset (n = 31,772), and 14% of all individuals who were arrested or charged during the study period (n = 7,493). Although this group made up less than 5% of the overall sample, they were shown to be involved in approximately 58% (n = 21,086) of all criminal interactions (n = 36,280), 26% (n = 46,961) of all non-criminal interactions (n = 177,071) and 32% (n = 68,047) of all police interactions (n = 213,351). Those in the chronic offending group had on average 19.8 criminal offences, and 64 police interactions across the five-year study period. Of note, over two-thirds (69%) of police interactions involving chronic offenders were non-criminal in nature, with non-criminal complaints being the largest category, accounting for 37.6% (n = 25,571) of all interactions for chronic offenders. The next most frequent category of interactions for this group was administrative offences, accounting for 19% of their police involvement, followed by tickets (11%), crimes against property (8.3%), and intimate partner and/or family disturbances (8.3%). Approximately 3.5% of interactions (n = 2,412) for the chronic offending group involved crimes against persons. The chronic offending group was also involved in a small proportion of victimization and witness cases, making up 1% and 0.2% of cases respectively.\u003c/p\u003e\n\u003cp\u003eUsing the cut-off of 10 or more police \u003cem\u003einteractions,\u003c/em\u003e our analyses revealed a second chronically involved group that was of equal size to the chronic offender group, but whose full police involvement was non-criminal in nature. The chronic non-offending group made up 3.5% (n = 1,097) of the sample and were involved in 11.8% of non-criminal police interactions and 9.8% (n = 20,946) of all police interactions included in the study. The chronic non-offending group were most often involved in intimate partner and family disturbance cases, making up 28.7% of all their police interactions. Being a witness was the second largest interaction category for the chronic non-offending group, making up approximately 20% of their interactions. Non-criminal complaints (18%), individual safety concerns (15.5%), and community safety and order (10.4%) were the next most frequent interactions for this group, followed by being a victim (4.3%) and tickets (2.5%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDifferences between chronic offenders and chronic non-offenders on demographics and measures of non-criminal police involvement\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGroups differences were observed on both demographic characteristics and non-criminal police involvement between the chronic offending and chronic non-offending groups (table two). Women made up 53% of the chronic non-offending group but only 22% of the chronic offending group (\u0026chi;\u003csup\u003e2\u003c/sup\u003e = 226.6, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). The chronic non-offending group was also shown to be slightly older ( \u0026nbsp;= 33.3), compared to the chronic offending group ( \u0026nbsp;= 30.5, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .001). Regarding police interaction frequency, the chronic offending group was shown to have significantly more non-criminal police interactions ( \u0026nbsp;= 44.2) across the five-year period, compared to the chronic non-offending group ( \u0026nbsp;= 19, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .001), which is a mostly unsurprising result given the difference in how these groups were operationalized (i.e., 10 or more criminal offences versus 10 or more interactions). When looking at the different types of non-criminal police interactions across groups, it was found that the chronic non-offending group had greater involvement with police due to concerns of individual safety (\u0026chi;\u003csup\u003e2\u003c/sup\u003e = 2.5, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), intimate partner and family conflict (\u0026chi;\u003csup\u003e2\u003c/sup\u003e = 2.9, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), victimization cases (\u0026chi;\u003csup\u003e2\u003c/sup\u003e = 549, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and witness cases (\u0026chi;\u003csup\u003e2\u003c/sup\u003e = 9.6, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). The chronic offending group was shown to have greater involvement in cases relating to community safety and order (\u0026chi;\u003csup\u003e2\u003c/sup\u003e = 19, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), complaints (\u0026chi;\u003csup\u003e2\u003c/sup\u003e = 7.9, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and tickets (\u0026chi;\u003csup\u003e2\u003c/sup\u003e = 2.5, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003eTable 2 About Here\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003e\u003cem\u003eGroup Comparisons on Demographics and Non-Criminal Police Interaction Variables\u003c/em\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"690\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eChronic\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNon-Offenders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eChronic Offenders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003et/\u003c/em\u003e\u003cem\u003e\u0026chi;\u003c/em\u003e\u003cem\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003ep-value\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003en = 1,097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003en = 1,061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003eGender (% women)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e586 (53%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e233 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e226.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003e\u0026nbsp;Age at Index (\u003cem\u003esd\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e33.3 (0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e30.5 (0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Criminal \u0026lsquo;Subject\u0026rsquo; Police Interactions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003en = 20,946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003en = 46,961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003eNumber of Non-criminal Interactions \u0026nbsp; (\u003cem\u003esd\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e19 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e44.2 (69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003eIndividual Safety Concerns (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3,244 (15.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2,024 (4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003eIntimate Partner \u0026amp; Family Conflict (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e6,023 (28.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5,637 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003eCommunity Safety \u0026amp; Order (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2,182 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5,430 (11.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e19\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003eComplaints (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3,753 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e25,571 (54.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 690px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lsquo;Victim\u0026rsquo; and \u0026lsquo;Witness\u0026rsquo; Police Interactions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003eVictimization Interactions (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e915 (4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e670 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003eWitness Interactions (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e4,312 (20.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e165 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 690px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTickets\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003eProvincial/Municipal Tickets (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e517 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e7,464 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePatterns of non-criminal police involvement for chronic offenders and chronic non-offenders.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eSimultaneous LCA models for the chronic non-offenders and chronic offenders were used to examine patterns of police interaction over time. Results for the two LCA models were similar in terms of the number of latent classes that emerged from the data, with both groups displaying a two-class model solution. Model selection was easier for the chronic offending group, as each of the fit indices provided clear evidence for a two-class model (table three). For the chronic non-offenders, the likelihood ratio chi-square, a statistical measure that describes whether the model can be generalized to the population, as well as one of the relative fit statistics, the AIC, suggested a four-class model. However, the BIC, the sample-size adjusted BIC, and the interpretability of the models pointed towards a two-class model. The researchers chose the more parsimonious and interpretable solution and thus decided that the two-class model was best fitting for the chronic non-offender group (table three). Overall, the simultaneous LCA provided evidence for partial measurement equivalence across the two groups, as both groups appeared relatively comparable in terms of the number of latent classes and class size, however, there were clear differences on item-response probabilities across the two groups (figure one).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 About Here\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003e\u003cem\u003eSimultaneous LCA global and relative fit statistics across study groups\u003c/em\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"680\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eClass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eG\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eaBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eEntropy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eSmallest Class Size (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eLMR LRT\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eBLRT\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 680px;\"\u003e\n \u003cp\u003eChronic Offending Group (n = 1,061)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e83.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5085.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e5110.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5094.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5025.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e5080.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5045.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e252 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5034.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e5118.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5064.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e234 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5043.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e5158.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5084.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e55 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5054.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e5198.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5106.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e25 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5064.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e5237.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5126.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e5 (\u0026lt; 1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 680px;\"\u003e\n \u003cp\u003eChronic Non-Offending Group (n = 1,097)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e253.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6235.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e6260.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6244.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e55.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6050.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e6105.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6070.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e139 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6039.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e6070.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6070.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e49 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e11.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6029.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e6144.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6071.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e59 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6032.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e6084.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6084.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e15 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6042.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e6106.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6106.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e17 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAcross both groups, the majority of individuals (76-87%) were categorized into the first class, representing a \u0026lsquo;high service needs\u0026rsquo; pattern of non-criminal police involvement. This first class was characterized by a moderate to high-level of involvement with police across each of the five police interaction categories. This first class appeared quite similar across the two groups, with slight differences on class prevalences and item-response probabilities for individual safety, community safety and order, complaints, and tickets interactions (figure one). More chronic non-offenders (87%) were categorized into this high-needs sub-group, compared to chronic offenders (76.5%), however, the chronic offenders presented with higher probabilities of endorsing each individual variable. According to these results, the majority of chronic non-offenders and offenders present to police with complex needs relating to individual safety, relational safety, and community safety.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe second class to emerge from the simultaneous LCA displayed both similarities and differences across the two chronically involved groups. The second class appeared relatively similar across groups in terms of class size, making up between 12% (for chronic non-offenders) and 23% (for chronic offenders) of group members. Across groups, individuals categorized in the second class had a low probability (0-.15) of presenting to police due to their individual safety, and a moderate probability (.50-.64) of interacting with police due to an intimate partner or family dispute case. For the chronic non-offenders, the second class was further characterized by a low probability (0.04) for community safety police involvement, a slightly higher probability (.15) of being involved in a non-criminal complaint, and a low probability (0.06) of being involved in an interaction that resulted in a ticket. For these individuals, intimate partner and family conflict appears to be the primary presenting concern. Although this second class makes up a smaller proportion of the chronic non-offending group (12.6%), they were also shown to be involved in significantly more non-criminal police interactions, compared to the first \u0026lsquo;high needs\u0026rsquo; class that emerged for the chronic non-offenders (t (1095) = 3.6, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) (figure one). The second class of chronic offenders displayed a more complex picture compared to the chronic non-offender, with these individuals having a moderate to high probability (.65) of being involved in a community safety concern, a high probability of being involved in a non-criminal complaint (.99), and a low-moderate probability (.32) of being ticketed. These individuals appear to have multiple presenting concerns, both with immediate intimate partner and family relationships, as well as within the community, and thus may require additional supports and resources, compared to the chronic non-offenders.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current study explored the non-criminal police interactions for both chronic offending and non-offending police involved persons across a five-year period in Ontario, Canada. In doing so, we confirmed the existence of the Pereto principle across multiple criminal justice measures, including criminal offence frequency and non-criminal police interaction frequency. As expected, we found that a small proportion of the police involved population were chronically involved in criminal offences. These individuals made up 3.3% of the overall police involved population, 14% of the offending population, and were shown to be involved in close to a third of all police interactions (32%) and over half of all criminal offences (58%). A second group that was of equal size to the chronic offenders were shown to be chronically involved with policing services, but for non-criminal concerns. These individuals made up 3.5% of the overall police involved population and were involved in ten-plus non-criminal police interactions, making up 9.8% of all police interactions. Both the chronic offending and chronic non-offending groups differed on age and gender, with chronic non-offenders shown to be slightly older and more likely to identify as female. The groups also differed on measures of non-criminal police interactions. Chronic non-offenders were most often involved with police due to issues relating to intimate partner and family conflict, witnessing police reported events, informally resolved complaints, and individual safety concerns. On the other hand, the chronic offender group was most often involved with police due to informally resolved complaints, tickets, intimate partner and family conflict, and concerns of community safety and order. Moreover, the chronic offender group was shown to be more highly involved, accumulating a significantly greater number of non-criminal police interactions, compared to the chronic non-offenders. These group differences on non-criminal police involvement have important implications for public safety policies and interventions, demonstrating that those who are chronically involved with police for non-criminal reasons likely have different, but sometimes overlapping, intervention needs compared to those who become chronically involved for both criminal and non-criminal issues. Future research should consider exploring intervention needs for individuals who become chronically involved with police due to intimate partner and family conflict, community complaints, or witnessing crime or other police reported events. In some regards, these group differences weren\u0026rsquo;t overly surprising, considering we used two different types of justice measures to identify two different groups of justice-involved persons, and thus there is likely unique differences between criminal and non-criminal justice-involved populations, as evidenced by our findings. For an example, building upon general strain theory of crime (Agnew, 2001; Agnew \u0026amp; Brezina, 2019), it\u0026rsquo;s possible that chronic offenders (compared to chronic non-offenders) have an elevated risk of developing anti-social outcomes due to their unique constellation of severe adverse experiences and unmet criminogenic needs for services. Additional research is needed to better understand the experiences of adversity and access to services for individuals who become chronically involved with police for both criminal and non-criminal concerns.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults from the latent class analyses revealed that each study group had two distinct subgroups based upon their non-criminal interaction patterns with police. The first class to emerge for both the chronic offenders and non-offenders was quite similar and included a majority of group members (76-87%). This first class displayed a high-needs pattern of non-criminal police interactions, where individuals were moderately to highly involved in police due to individual safety concerns, intimate partner and family conflict, community safety and order concerns, and non-criminal complaints. Differences between the chronic offenders and non-offenders were most prevalent within the second latent class. For the chronic non-offenders, the second class involved 12.6% of group members who were largely involved with police due to intimate partner and family conflict, with a smaller probability of being involved in non-criminal complaints. For the chronic offenders, the second class involved 24% of group members who were involved with police for a variety of concerns, including intimate partner and family conflict, as well as community safety, complaints, and tickets. These results demonstrate that most individuals who become chronically involved with police are of high need for supportive services. Three of the four sub-groups to emerge, including the entirety of the chronic offender group and 87% of the chronic non-offender group, displayed a high-needs pattern of non-criminal police interactions, characterized by a moderate-to-high level probability of interacting with police across multiple types of presenting concerns.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNon-criminal police interactions: A meaningful analytical unit to better understand chronic police involvement\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs demonstrated by our results, non-criminal police interactions make up an important analytical unit for understanding the full scope of police involvement for those who are repeatedly or chronically involved with the police. This comes as no surprise, given that policing services in Canada are meant to respond to a wide variety of non-criminal concerns to protect and promote public safety and order. In addition, prior research has shown that chronic offending samples are typically made up of individuals who have experienced severe adversity, maladjustment, and missed opportunities (Fox et al., 2015; Zara et al., 2016, 2019). Thus, it is likely this population has higher needs for services compared to the general population, and that this unmet need for services may be reflected in higher levels of police interaction. Moreover, the existence of both chronic offending and non-offending groups within the data, and the fact that non-criminal incidents made up the majority of chronic offenders\u0026rsquo; contact with police, provides further support for the utility of justice system measures that go beyond criminal offending. Our analyses suggest that both chronic offending and non-offending groups are in frequent contact with police due to non-criminal complaints, intimate partner and family conflict, community safety and order concerns, and individual health and safety all of which may serve as potential therapeutic intervention targets to address the issue of chronic police involvement.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy attending to the full police interaction record we can temporarily shift our analytical perspective from \u0026lsquo;individual problem behavior\u0026rsquo; (i.e., criminal offending) to \u0026lsquo;system utilization patterns\u0026rsquo;. This can allow researchers, practitioners, and policy makers to zoom out and observe a more complete picture of how and why individuals become repeatedly involved in the justice system. This is not to say that criminal offending measures should be replaced, rather, they should be supplemented with other available information. Secondly, measures of non-criminal police interactions may be useful in evaluating policing services, including the deployment of policing resources, and the efficacy of reliance on a police response across different types of calls for service. This data may support public health and safety goals to develop evidence-based policing responses, and more effective coordination between policing, community services, and other public health and safety services (i.e., health, mental health, child welfare) (Pepler \u0026amp; Macnamara., 2024). The numbers presented in our results are helpful and relevant. In the community in which this police service is located, there are approximately 1000 individuals who were chronically involved with police for non-criminal reasons, and an additional 1000 individuals who were involved for both non-criminal and criminal reasons. Together, these groups are responsible for almost 90,000 interactions with police over five years, or approximately 18,000 interactions per year. The study adds to the voices of prior researchers and advocates (Ellingwood, 2015, Pepler et al., 2024) in concluding that the lives and costs associated with this level of police service demands robust community-police collaborations to meet the needs of this group of individuals.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLimitations\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, it\u0026rsquo;s important to acknowledge the limitations of using police UCR data. This type of data has limited information on the people involved with the police, the precipitating events that resulted in their involvement, and the outcomes of the interaction. This study was missing information on how exactly police cleared each interaction and whether they made a service referral during the interaction. We were also missing important information pertaining to individuals\u0026rsquo; demographics, including non-binary gender identity, sexuality, race, and ethnicity. It is also important to acknowledge that there is a lack of standardization of police data collection and sharing practices across organizations within Canada (Huey, Ferguson, \u0026amp; Vaughan, 2021). This greatly reduces the ability to compare results across policing jurisdictions. Finally, it\u0026rsquo;s important to acknowledge the limitations of the chronic police involvement measurements that were applied in this study. Previous research has shown that ten-plus criminal offences is an adequate measure for chronic offending, however, no such research exists for chronic non-offending, and thus there may be alternative measures of chronic non-offending police involvement that should be considered in future research. Moreover, our results demonstrated that chronic offending and non-offending groups differed on both demographics and police involvement measures at the bivariate level. These comparisons may have been driven by the fact that we used different types of justice data measurements to identify two different groups of justice-involved persons. Additional research on chronic non-offending populations, and how they might differ from offending populations, is needed to better understand these two unique justice-involved populations and their needs for public services. \u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003ePrior research has extensively covered the topic of chronic offending; however, little is known about how chronic offending populations interact with police beyond their involvement in crime. Prior research is also lacking on the extent to which non-offending populations become chronically involved with the police for non-criminal matters. Future research should continue exploring chronic police involvement for offending and non-offending police involved populations. Research is needed to explore childhood and youth involvement with police more broadly beyond their involvement in delinquency and crime, and whether childhood and youth populations are becoming chronically involved with police. Future research should also explore chronic police involvement for specific populations, including individuals with severe health and mental health disorders. It\u0026rsquo;s important to disentangle police involvement for these populations and to make distinctions between police involvement for help seeking versus police involvement for delinquency and crime. Finally, future research should continue to advocate for increased public service data infrastructure that can better connect and integrate data from across public services, supporting our understanding of public service utilization patterns, and how to better coordinate services to promote public health and safety.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA.W. wrote the main manuscript text with the support and supervision from K.S. and L.H. All authors reviewed and made edits to the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe authors report there are no competing interests to declare\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eFunding declaration: \u003cem\u003eNo funding was received to support this work.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgnew, R. (2001). Building on the foundation of general strain theory: Specifying the types \u003c/li\u003e\n\u003cli\u003eof strain most likely to lead to crime and delinquency. \u003cem\u003eJournal of research in crime and delinquency\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(4), 319-361.\u003c/li\u003e\n\u003cli\u003eAgnew, R., Brezina, T. (2019). General Strain Theory. In: Krohn, M., Hendrix, N., Penly Hall, G., Lizotte, A. (eds) Handbook on Crime and Deviance. Handbooks of Sociology and Social Research. Springer, Cham. https://doi.org/10.1007/978-3-030-20779-3_8\u003c/li\u003e\n\u003cli\u003eAkaike, H. (1987). Factor analysis and AIC. \u003cem\u003ePsychometrika\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e, 317-332.\u003c/li\u003e\n\u003cli\u003eAkins, S., Burkhardt, B. C., \u0026amp; Lanfear, C. (2016). Law enforcement response to \u0026lsquo;frequent fliers\u0026rsquo; an examination of high-frequency contacts between police and justice-involved persons with mental illness. \u003cem\u003eCriminal justice policy review\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(1), 97-114.\u003c/li\u003e\n\u003cli\u003eBlumstein, A., Farrington, D. P., \u0026amp; Moitra, S. (1985). Delinquency careers: Innocents, desisters, and persisters. \u003cem\u003eCrime and justice\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e, 187-219.\u003c/li\u003e\n\u003cli\u003eCanadian Centre for Justice Statistics (2019). Uniform Crime Reporting Survey (UCR) Manual. Statistics Canada.\u003c/li\u003e\n\u003cli\u003eCarrington, P. J., Matarazzo, A., \u0026amp; DeSouza, P. (2005). \u003cem\u003eCourt careers of a Canadian birth cohort\u003c/em\u003e. Ottawa, ON: Statistics Canada, Canadian Centre for Justice Statistics.\u003c/li\u003e\n\u003cli\u003eCollins, L. M., \u0026amp; Lanza, S. T. (2009). \u003cem\u003eLatent class and latent transition analysis: With applications in the social, behavioral, and health sciences\u003c/em\u003e. John Wiley \u0026amp; Sons.\u003c/li\u003e\n\u003cli\u003eCorsaro, N. (2018). More than lightning in a bottle and far from ready-made. \u003cem\u003eCriminology \u0026amp; Pub. Policy\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e, 251.\u003c/li\u003e\n\u003cli\u003eDay, D. M., Bevc, I., Duchesne, T., Rosenthal, J. S., Sun, Y., Theodor, F., ... \u0026amp; Geguzinskis, D. (2007, June). Criminal trajectories from adolescence to adulthood in an Ontario sample of offenders. \u003cem\u003eCanadian Psychological Association/North American Correctional and Criminal Justice Psychology Conference (Ottawa, Ontario, 7th June 2007)\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eDay, D. M., Nielsen, J. D., Ward, A. K., Sun, Y., Rosenthal, J. S., Duchesne, T., ... \u0026amp; Rossman, L. (2012). Long-term follow-up of criminal activity with adjudicated youth in Ontario: Identifying offence trajectories and predictors/correlates of trajectory group membership. \u003cem\u003eCanadian Journal of Criminology and Criminal Justice\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e(4), 377-413.\u003c/li\u003e\n\u003cli\u003eDudfield, G., Angel, C., Sherman, L. W., \u0026amp; Torrence, S. (2017). The \u0026lsquo;power curve\u0026rsquo; of victim harm: Targeting the distribution of crime harm index values across all victims and repeat victims over 1 year. \u003cem\u003eCambridge Journal of Evidence-Based Policing\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e, 38-58.\u003c/li\u003e\n\u003cli\u003eEllingwood, H. (2016). \u003cem\u003eA better estimation of police costs by offence types\u003c/em\u003e. Public Safety Canada.\u003c/li\u003e\n\u003cli\u003eFarrington, D. P., \u0026amp; West, D. J. (1993). Criminal, penal and life histories of chronic offenders: Risk and protective factors and early identification. \u003cem\u003eCriminal behaviour and mental health\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(4), 492-523.\u003c/li\u003e\n\u003cli\u003eFarrington, D. P., \u0026amp; Ttofi, M. M. (2012). Protective and promotive factors in the development of offending. In T. Bliesener, A. Beelmann, \u0026amp; M. Stemmler (Eds.), \u003cem\u003eAntisocial behavior and crime: Contributions of developmental and evaluation research to prevention and intervention\u003c/em\u003e (pp. 71\u0026ndash;88). Hogrefe Publishing.\u003c/li\u003e\n\u003cli\u003eFox, B. H., Perez, N., Cass, E., Baglivio, M. T., \u0026amp; Epps, N. (2015). Trauma changes everything: Examining the relationship between adverse childhood experiences and serious, violent and chronic juvenile offenders. \u003cem\u003eChild abuse \u0026amp; neglect\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e, 163-173.\u003c/li\u003e\n\u003cli\u003eHaczkewicz, K. M., Shahid, S., Finnegan, H. A., Moninn, C., Cameron, C. D., \u0026amp; Gallant, N. L. (2024). Adverse childhood experiences (ACEs), resilience, and outcomes in older adulthood: A scoping review. \u003cem\u003eChild Abuse \u0026amp; Neglect\u003c/em\u003e, 106864.\u003c/li\u003e\n\u003cli\u003eHoch, J. S., Hartford, K., Heslop, L., \u0026amp; Stitt, L. (2009). Mental illness and police interactions in a mid-sized Canadian city: What the data do and do not say. \u003cem\u003eCanadian Journal of Community Mental Health\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(1), 49-66.\u003c/li\u003e\n\u003cli\u003eHowe, B., \u0026amp; Covell, K. (2008). \u003cem\u003eChildren, Families and Violence: Challenges for Children\u0026apos;s Rights\u003c/em\u003e. Jessica Kingsley Publishers.\u003c/li\u003e\n\u003cli\u003eHuey, L., \u0026amp; Ferguson, L. (2023). \u0026lsquo;Did not return in time for curfew\u0026rsquo;: A descriptive analysis of homeless missing persons cases. \u003cem\u003eInternational Criminal Justice Review\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(1), 87-101.\u003c/li\u003e\n\u003cli\u003eHuey, L., Ferguson, L., \u0026amp; Vaughan, A. D. (2021). The limits of our knowledge: tracking the size and scope of police involvement with persons with mental illness. \u003cem\u003eFacets\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 424-448\u003c/li\u003e\n\u003cli\u003eHuey, L., \u0026amp; Ricciardelli, R. (2016). From seeds to orchards: Using evidence-based policing to address Canada\u0026rsquo;s policing research needs. \u003cem\u003eCanadian journal of criminology and criminal justice\u003c/em\u003e, \u003cem\u003e58\u003c/em\u003e(1), 119-131.\u003c/li\u003e\n\u003cli\u003eHuey, L., Schulenberg, J. L., \u0026amp; Koziarski, J. (2022). \u003cem\u003ePolicing Mental Health: Public Safety and Crime Prevention in Canada\u003c/em\u003e. Springer.\u003c/li\u003e\n\u003cli\u003eIacobucci, F. (2014). \u003cem\u003ePolice Encounters with People in Crisis\u003c/em\u003e. Toronto Police Service.\u003c/li\u003e\n\u003cli\u003eIbrahim, D. (2019). Youth re-contact with the Nova Scotia justice system, 2012/2013 to 2014/2015. \u003cem\u003eJuristat: Canadian Centre for Justice Statistics\u003c/em\u003e, 1-18.\u003c/li\u003e\n\u003cli\u003eKalmakis, K. A., \u0026amp; Chandler, G. E. (2015). Health consequences of adverse childhood experiences: A systematic review. \u003cem\u003eJournal of the American Association of Nurse Practitioners\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(8), 457-465.\u003c/li\u003e\n\u003cli\u003eKouyoumdjian, F. G., Wang, R., Mejia-Lancheros, C., Owusu-Bempah, A., Nisenbaum, R., O\u0026rsquo;Campo, P., ... \u0026amp; Hwang, S. W. (2019). Interactions between police and persons who experience homelessness and mental illness in Toronto, Canada: findings from a prospective study. \u003cem\u003eThe Canadian Journal of Psychiatry\u003c/em\u003e, \u003cem\u003e64\u003c/em\u003e(10), 718-725.\u003c/li\u003e\n\u003cli\u003eKoziarski, J., Ferguson, L., \u0026amp; Huey, L. (2022). Shedding light on the dark figure of police mental health calls for service. \u003cem\u003ePolicing: A Journal of Policy and Practice\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(4), 696-706.\u003c/li\u003e\n\u003cli\u003eKuha, J. (2004). AIC and BIC: Comparisons of assumptions and performance. \u003cem\u003eSociological methods \u0026amp; research\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(2), 188-229.\u003c/li\u003e\n\u003cli\u003eLo, Y., Mendell, N. R., \u0026amp; Rubin, D. B. (2001). Testing the number of components in a normal mixture. \u003cem\u003eBiometrika\u003c/em\u003e, \u003cem\u003e88\u003c/em\u003e(3), 767-778.\u003c/li\u003e\n\u003cli\u003eMazowita, B., \u0026amp; Rotenberg, C. (2019). The Canadian police performance metrics framework: Standardized indicators for police services in Canada. \u003cem\u003eJuristat: Canadian Centre for Justice Statistics\u003c/em\u003e, 1-13.\u003c/li\u003e\n\u003cli\u003eMcLachlan, G. J., \u0026amp; Peel, D. (2000). \u003cem\u003eFinite mixture models\u003c/em\u003e. John Wiley \u0026amp; Sons.\u003c/li\u003e\n\u003cli\u003eMuth\u0026eacute;n, B. (2004). Latent variable analysis. \u003cem\u003eThe Sage handbook of quantitative methodology for the social sciences\u003c/em\u003e, \u003cem\u003e345\u003c/em\u003e(368), 106-109.\u003c/li\u003e\n\u003cli\u003eNagin, D. S., \u0026amp; Odgers, C. L. (2010). Group-based trajectory modeling in clinical research. \u003cem\u003eAnnual review of clinical psychology\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 109-138.\u003c/li\u003e\n\u003cli\u003eNylund, K. L., Asparouhov, T., \u0026amp; Muth\u0026eacute;n, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. \u003cem\u003eStructural equation modeling: A multidisciplinary Journal\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(4), 535-569.\u003c/li\u003e\n\u003cli\u003eOlmstead, B., Hoffman, R., Brown, G. P., \u0026amp; Hirdes, J. P. (2022). Using the interRAI brief mental health screener to identify persons with mental disorders having repeat contact with police. \u003cem\u003eInternational journal of law and psychiatry\u003c/em\u003e, \u003cem\u003e83\u003c/em\u003e, 101816.\u003c/li\u003e\n\u003cli\u003ePepler, E. F., \u0026amp; Barber, C. G. (2021). Mental health and policing: picking up the pieces in a broken system. In \u003cem\u003eHealthcare Management Forum\u003c/em\u003e (Vol. 34, No. 2, pp. 93-99). Sage CA: Los Angeles, CA: SAGE Publications.\u003c/li\u003e\n\u003cli\u003ePepler, E., \u0026amp; Macnamara, W. D. (2024, March). Our public safety system is a perfect storm. In \u003cem\u003eHealthcare Management Forum\u003c/em\u003e (Vol. 37, No. 2, pp. 56-62). Sage CA: Los Angeles, CA: SAGE Publications.\u003c/li\u003e\n\u003cli\u003ePiquero, A. R. (2008). Taking stock of developmental trajectories of criminal activity over the life course. In \u003cem\u003eThe long view of crime: A synthesis of longitudinal research\u003c/em\u003e (pp. 23-78). New York, NY: Springer New York.\u003c/li\u003e\n\u003cli\u003ePiquero, A. R., Farrington, D. P., \u0026amp; Blumstein, A. (2007). \u003cem\u003eKey issues in criminal career research: New analyses of the Cambridge Study in Delinquent Development\u003c/em\u003e. Cambridge University Press.\u003c/li\u003e\n\u003cli\u003eSclove, S. L. (1987). Application of model-selection criteria to some problems in multivariate analysis. \u003cem\u003ePsychometrika\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e(3), 333-343.\u003c/li\u003e\n\u003cli\u003eStanding Committee on Public Safety and National Security (2014). Economics of Policing. House of Commons, Canada, 41\u003csup\u003est\u003c/sup\u003e Parliament, Second Session.\u003c/li\u003e\n\u003cli\u003eTracy, P. E., Wolfgang, M. E., Figlio, R. M. (1990). The Chronic Juvenile Offender. \u003cem\u003eDelinquency Careers in Two Birth Cohorts\u003c/em\u003e, 81-97.\u003c/li\u003e\n\u003cli\u003eAuthor r.42. (2024). \u003c/li\u003e\n\u003cli\u003eWhitten, T., McGee, T. R., Homel, R., Farrington, D. P., \u0026amp; Ttofi, M. (2019). Comparing the criminal careers and childhood risk factors of persistent, chronic, and persistent\u0026ndash;chronic offenders. \u003cem\u003eAustralian \u0026amp; New Zealand journal of criminology\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e(2), 151-173.\u003c/li\u003e\n\u003cli\u003eWilson-Bates, F., \u0026amp; Chu, J. (2008). \u003cem\u003eLost in transition: How a lack of capacity in the mental health system is failing Vancouver\u0026apos;s mentally ill and draining police resources\u003c/em\u003e. Vancouver Police Department.\u003c/li\u003e\n\u003cli\u003eWolfgang, M. E., Figlio, R. M., \u0026amp; Sellin, T. (1972). \u003cem\u003eDelinquency in a birth cohort\u003c/em\u003e. University of Chicago Press.\u003c/li\u003e\n\u003cli\u003eZara, G., \u0026amp; Farrington, D. P. (2016). Chronic offenders and the syndrome of antisociality: Offending is only a minor feature! \u003cem\u003eIrish Probation Journal\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e, 40-64.\u003c/li\u003e\n\u003cli\u003eZara, G., \u0026amp; Farrington, D. P. (2019). Unsuccessful lifestyle in middle-aged official and self-reported types of offenders. \u003cem\u003eJournal of Criminal Justice\u003c/em\u003e, \u003cem\u003e64\u003c/em\u003e, 101624.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Non-criminal ticket offences represent instances where an individual broke a municipal by law (e.g., excessive noise, animal control) or a provincial regulations law (e.g., trespassing, environmental protections, occupational health and safety, traffic violations).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e These interactions are often not formally documented in a police occurrence report and are missing information regarding the occurrence description and UCR code, therefore, it\u0026rsquo;s impossible to know what type of circumstances initiated these police interactions, however, we can ascertain that they were of a less serious nature.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"chronic offending, police, domestic disturbance, police involvement","lastPublishedDoi":"10.21203/rs.3.rs-7829955/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7829955/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Prior research has extensively discussed the issue of chronic offending, where a small group of individuals are repeatedly charged by police and are involved in large numbers of criminal offences. Considerably less research explores individuals who are chronically involved with the police for non-criminal reasons. Using police records data, the current study describes the chronic police involvement for both criminal and non-criminal police involved populations. Using a prospective longitudinal design, a baseline sample of 31,755 individuals who were involved with police in 2015, were followed through the police record until 2020. Chronic offenders made up 3.3% of the overall police involved population and were involved in close to one third of all police interactions and over half of all criminal offences. A second group, chronic non-offenders, were also shown to be chronically involved with policing services, but for non-criminal matters. These individuals made up 3.5% of the overall police involved population and were involved in approximately ten percent of all police interactions. Both the chronic offending and chronic non-offending groups differed on measures of demographics and non-criminal police involvement. Policy and practice implications surrounding the use of police data for improved service coordination and prevention insights are discussed.","manuscriptTitle":"Chronic Involvement with Canadian Policing Services Beyond Criminal Offending","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-23 04:37:49","doi":"10.21203/rs.3.rs-7829955/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"45a645f6-bb97-4bfe-97a4-bfb301921cb6","owner":[],"postedDate":"February 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T00:08:22+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-23 04:37:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7829955","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7829955","identity":"rs-7829955","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}