Who keeps us safe? A critical examination of the role of contextual dimensions in lethal outcomes of police involvement in mental health crises response among Indigenous peoples in Canada
Bibliographic record
Abstract
Persons living with mental illness and Indigenous people are over-represented in police-involved fatalities, yet few studies have examined these intersections together. In Canada, Indigenous persons are susceptible to police violence and death, due to a myriad of factors, such as systemic racism, community surveillance, and proximity to police departments overinvolved in the use of force. Using quantitative content analysis of media reports of Indigenous people involved in police-based mental health emergency response, this study examined demographic and contextual factors associated with fatal outcome. All cases included media articles published between the year 1970 and 2022. McNemar’s Chi Square tests were conducted with categorical variables that violated the assumption of independence. All remaining variables were entered into a binary logistic model, which explained 40% of the variance and found statistically significant associations with lethality for multiple age ranges. No differences were observed for sex, region, or type of police service.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".