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In Canada, people who experience mental health disability are over-represented among those who die in police interactions. At present, there is sparse national data available about the nature and extent of police-involved deaths of persons who experience mental health disability. Using mixed methods including access to information requests, media analysis and thematic analysis, this study analyzes data from coroner and medical examiner reports produced from 2004 to 2014, including descriptions of 433 police-involved deaths in Canada to explore the over-representation of people who experience mental health disability. Secondly, it provides a review of contemporary case law on legal liability and accountability of police, comparing traditional tort law and criminal prosecution approaches with the potential for discrimination claims on the ground of disability. This review focuses on police use of force and considers the benefits and drawbacks to tort, criminal and human rights law as means of legal accountability for police-involved injuries and deaths of persons who experience mental health disability. Employing a typology of police-involved deaths, it is demonstrated that persons who are perceived to experience mental health impairment are over-represented among several types of police-involved deaths subject to coroner or medical examiner proceedings. Further, it is suggested that recommendations stemming from coroner and medical examiner reports offer few helpful suggestions for meaningful reform to prevent such deaths. Lastly, it is suggested that discrimination claims are unlikely to prove more successful than tort claims or criminal prosecution as a means of sparking police reform. However, recognition of the potential for systemic discrimination by police should cause governments to re-evaluate police practices and societal strategies to address mental health crises.

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Police paramilitary units emerged as a tool for police agencies during the 1980’s to deal with dangerous calls for service such as hostage situations, barricaded persons, terrorism and sniper situations. American policing scholars began tracing the annual call outs of tactical units to determine if tactical policing has shifted away from its original purpose towards a more proactive police response. Canadian literature on police militarization is limited but follows similar American research trends by focusing on the annual callouts of Canadian tactical teams to prove that tactical policing has been normalized. This project uses Bourdieusian concepts (field struggle, capital, symbolic power) to address gaps in the existing literature surrounding police militarization by determining the strategies and capital used by community groups and the police to defend and contest police militarization in Canada. This helped answer the primary research question of: What arguments do police, government officials and civil society groups make to support or contest police militarization? What forms of power and symbolic power are utilized to shift opinion? Relying on a Critical Content Analysis, this project established deductive categories from newspaper articles, recommendations from Independent Inquiries and media releases from community activist websites to understand the contests of police militarization. Findings from this project were similar to previous literature regarding the arguments mobilized by the police about police militarization; however, community groups played a more active role in contesting the dominant agendas of state actors through mobilizing moral and emotional arguments. Emotional arguments were influenced by the deaths of George Floyd and Michael Brown and allowed community groups to contest police militarization through defunding and demilitarization arguments. Moreover, findings show that both the police and community groups use community safety arguments to contest and defend police militarization. Legal arguments were also mobilized by relevant parties to address Indigenous human rights violations produced through militarized police dynamic entries. The findings also showed the impacts of high-profile cases on trends within police militarization. The Independent Inquiries following high profile cases such as the Moncton (2014) and Mayerthorpe (2005) shootings influenced military equipment procurement and SWAT standards across Canada. A Political Sociological analysis of my findings revealed the struggle within the field of police militarization regarding the procurement of militarized equipment. This project provided a snapshot of police militarization in Canada to help understand the ongoing militarization issues as well as the relevant actors who are involved in the discourse. Directions for future research are presented at the end of this study.

My objective was to predict outcomes following police interactions with PMIs, and compare the predictive accuracy of logistic regression models and Random Forests learning algorithms. Additionally I evaluated if predictive accuracy of Random Forests changed when applied to merged versus region-specific data. I conducted a retrospective cohort study of reports completed by police in 13 communities between 2015 and 2018. 13,058 reports were analyzed. Random Forests learning algorithms were compared against logistic regression models for predictive accuracy in a merged dataset (13 communities) and 3 regional datasets. Outcomes for prediction were high risk of harm to self, risk of harm to others, and risk of failure to care for self within 24 and 72 hours following police contact. Random Forests learning algorithms were trained on merged and regional datasets, and compared against merged and regional holdout datasets. Performance was compared by area under the curve. For Random Forests learning algorithms, confusion matrix statistics were calculated for each outcome and predictive utility was examined by calculating conditional probabilities. Prediction accuracy was modest across all methods. Random Forests achieved better predictive accuracy than logistic regression. Random Forests accuracy varied between merged and regional holdout data. Sensitivity of Random Forests learning algorithms were moderate (74% average, 6 outcomes, merged holdout set). Specificity was low (53% average, 6 outcomes, merged holdout set). Conditional probabilities were modestly improved by the use of the Random Forests learning algorithm. The rareness of the target outcomes created a situation where even predictions with moderate likelihood ratios had only modest predictive value. Though the Random Forests learning algorithms did outperform the logistic regression learning algorithms, the clinical significance of those benefits were limited when conditional probabilities were calculated. These findings are limited to the outcomes considered, and may not apply to more common outcomes. / Thesis / Master of Health Sciences (MSc) / The study goal was to predict outcomes following police interactions with persons with mental illness (PMIs). Additionally we compare the predictive validity of logistic regression and Random Forests learning algorithms. Classification approaches were applied to outcomes following police interactions with PMIs, including: high risk of harm to self, high risk of harm to others, and high risk of failure to care for self within 24 hours and 72 hours of initial police contact. The study also sought to determine if the predictive accuracy of Random Forests was sensitive to the police service community. Variation in predictive accuracy was assessed between a merged data set (13 communities) and 3 community-specific data. The study found that the predictive accuracy of the classification approaches on outcomes was modest. Random Forests exhibited greater predictive validity than logistic regression. The performance of the Random Forests suggested that performance was not sensitive to police service context.