EXPLORING CASE VARIABLES PREDICTIVE OF HISTORIES OF MENTAL ILLNESS IN INCIDENTS OF POLICE-INVOLVED FIREARM FATALITIES IN CANADA
Bibliographic record
Abstract
The tragedy of police-involved fatalities resulting in the death of individuals with serious mental illness has been brought to the forefront by recent high-profile incidents that have galvanized public concern and criticism that law enforcement organizations must improve their response to people in psychiatric crisis. This thesis employed descriptive and hierarchical logistic regression analyses to understand cases of police-involved shooting fatalities in Canada between 2006 and 2015. More precisely, this research focused on determining whether particular variables predicted group membership between victims with and without a history of mental illness. The General Aggression Model (GAM; Allen, Anderson, & Bushman, 2018) was used as a framework to understand how the presence of mental illness could impact police officers’ use of firearms in the course of their duties. Descriptive analyses revealed that police-involved firearms fatalities were on the rise in Canada and have increased faster over time for people with mental illness (PMI) compared to those without a history of mental illness. Hierarchical logistic regression analysis revealed that weapon type, ethnicity, and suicide-related behaviors were significant predictors of PMI being fatality shot by police officers as compared to victims without mental illness (correct classification 74.1%). Implications of the rising number of PMI involved in fatal shooting encounters with police and the unique predictors that underlie these lethal encounters are discussed considering the Behavioral Influence Stairway Model (Vecchi, Van Hasselt & Romano, 2005).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".