Therapeutic alignments: examining police and public health/harm reduction partnerships
Bibliographic record
Abstract
Ongoing calls for police reform across North America alongside the growing momentum for the removal of criminal sanctions for personal possession of drugs have placed policing agencies in an ambivalent position with respect to drug governance and people who use drugs (PWUD). Meanwhile, in response to the longstanding harms produced by drug law enforcement, calls for harm reduction policing have gained traction in recent years, resulting in collaborations between policing agencies and health services, including naloxone administration by police officers, post-overdose outreach and wellness checks, and integrated public health-public safety response and information sharing frameworks. Using situational analysis method, we consider the range of elements and actors that form these partnerships, and their broader structural, institutional, and policy effects. We detail the actual and potential implications of such forms of institutional coordination on health, equity, and the possibility of meaningful drug law reform. Our analysis reveals that rather than mitigating the harms of drug enforcement, such initiatives stand to undermine access to services and increase health system avoidance by eroding trust in public health and harm reduction among PWUD. We reason that the recasting of police as therapeutic agents and as embedded in medico-therapeutic practices reaffirms the role of punitive enforcement practices in drug governance.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".