A scoping review of harm reduction training for police officers
Bibliographic record
Abstract
ISSUES: Preventable overdose deaths, especially due to opioids, have increasingly been reported worldwide. Expansion of life-saving harm reduction services is underway with increasing public support in some jurisdictions. However, such services often fall short of reaching people who use drugs (PWUD), in part, due to law enforcement practices that are aligned with punitive drug laws and incongruent with harm reduction principles. One suggested strategy to facilitate police understanding and uptake of practices that are more congruent with harm reduction is to provide police with relevant training. APPROACH: This scoping review synthesises English-language peer-reviewed and grey literature on harm reduction training programs for police. KEY FINDINGS: We reviewed 31 sources and found that most trainings covered topics related to harm reduction objectives, overdose recognition and response, occupational safety and policing practices. Information was often presented via single-session, 1-hour long, slide-assisted presentations that were integrated into in-service trainings. Inconsistent throughout the literature was the career stage or position/rank of training audience (e.g. cadets, senior officers, street-level officers), when and how much training should be provided, and the occupational background of the training facilitator. IMPLICATIONS: The available literature contains significant gaps pertaining to descriptions of training development, design and content specific to facilitating positive police-PWUD interactions, and formal evaluations. These gaps limit our understanding of what well-designed trainings may look like, if and how training alters policing practices, and to what extent training completion may lead to improved outcomes. CONCLUSION: Greater research and formal evaluations of harm reduction training for police is recommended.
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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 | 0.016 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".