Turning a blind eye: implementation of harm reduction in a transitional programme setting
Bibliographic record
Abstract
Background: Substance use and homelessness often overlap. Harm reduction is an evidence-based response to reducing harms of use rather than preventing use per se. While harm reduction is a key principle of Housing First, there has been limited research related to the implementation of harm reduction within programmes that aim to end homelessness.Methods: As part of a community-based research study, we conducted 29 qualitative interviews with staff and residents in two transitional programmes. We used interpretive description to analyze the data and generate practice relevant recommendations.Results: Being abstinent was not a criterion for programme eligibility but participants identified “getting and coming clean” as a programme goal. While harm reduction was official policy and safer use supplies were available, there was a zero tolerance policy for substance use onsite and sanctions for use. In the wake of a persistent culture of abstinence and conflicting policies related to substance use and harm reduction, staff turned a blind eye to substance use in an effort to prevent harms.Conclusion: The findings of this study highlight tensions between abstinence and harm reduction policies and the problems associated with the implementation of harm reduction as waiving requirements for sobriety, distribution of clean supplies, and tolerating discrete use in an environment that restricts use as fulfilling harm reduction mandates. Partial or incomplete implementation of harm reduction and a failure to shift norms related to substance use and harm reduction may contribute to harms of overdose, intoxication and stigma as well as tensions between residents.
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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 | 0.023 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| 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".