Total systems failure: police officers’ perspectives on the impacts of the justice, health, and social service systems on people who use drugs
Bibliographic record
Abstract
BACKGROUND: Police in Canada have become main responders to behavioural health concerns in the community-a role that disproportionately harms people who use drugs (PWUD). Recent calls to defund the police emphasize the need to shift responsibility for non-criminal health issues from police to health and social services. This study explores the role of police interactions in responding to PWUD within the broader institutional and structural contexts in which they operate. METHODS: We conducted a qualitative thematic analysis of interviews with sixteen police officers across nine jurisdictions in British Columbia, Canada. We examined police officers' everyday policing experiences interacting with PWUD, enforcing drug laws, and working alongside other service sectors. RESULTS: Officers explained that the criminal justice system is one component of a wider network of systems that collectively fail to meet the needs of PWUD. They recognized that PWUD who interact with police often experienced intersecting structural vulnerabilities such as poverty, homelessness, and intergenerational trauma. Harmful drug laws in conjunction with inadequate treatment and housing resources contributed to a funnelling of PWUD into interactions with police. They provided several recommendations for reform including specialized health and justice roles, formalized intersectoral collaboration, and poverty reduction. CONCLUSIONS: Overall, this study provides unique insights into the positioning and role of police officers within a "total systems failure" that negatively impact PWUD. Police have become responders-by-default for issues that are fundamentally related to people's health conditions and socioeconomic circumstances. Addressing failures across the health, social, and justice systems to meet the needs of PWUD will require an examination of the shortcomings across these systems, as well as substantial funding and system reforms.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".