Criminalization and Care: The Limits of a Public Health Approach to Addressing Drug Use
Bibliographic record
Abstract
The attempt by public health programs to reduce harm from illicit drug use faces several barriers, particularly structural barriers stemming from the ongoing criminalization of drug use. The ways in which structural barriers impact the uptake and implementation of key harm reduction programs is an ongoing area of concern, particularly as Canada faces a devastating opioid overdose crisis. Using ethnographic observation and qualitative interviews, this dissertation examines a harm reduction intervention where the homes of people who use drugs become ‘satellite’ harm reduction programs – the Satellite Sites. This program uses well-known community members as harm reduction workers to provide harm reduction education and equipment directly in the community settings where people gather to buy, use, and sell drugs. The Satellite Sites straddle two worlds: they are sites of illicit and stigmatized activities, while also being sites of public health intervention. Satellite Sites are partially medicalized due to their association with the community health centre running the program. However, the continued criminalization of drug possession and distribution negatively impacts the uptake and effectiveness of public health recommendations within the Satellite Sites. Overdose education and naloxone distribution campaigns provide an example of how criminalization negatively impacts on uptake of public health recommendations. These campaigns aim to prevent morbidity and mortality from opioid overdose, and findings show that naloxone access is lifesaving. However, these programs may inadvertently exacerbate structural vulnerabilities among people who use drugs, including vulnerability to housing loss and negative emotional impacts from responding to multiple overdoses. This dissertation also explores practices of care among a criminalized group: people who sell drugs. The Satellite Site program employs people who sometimes sell drugs as harm reduction workers, rendering their practices of care visible. The integration of people who sell drugs into harm reduction programming remains underexplored, and may hold promise in addressing high overdose death rates. While the Satellite Sites extend harm reduction into the spaces in the community where people gather to use drugs, criminalization exacerbates existing structural vulnerabilities among people who use drugs, and impedes the attempt of public health programming to intervene to reduce drug-related harms.
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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 | 0.087 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.021 | 0.121 |
| Scholarly communication | 0.030 | 0.032 |
| Open science | 0.008 | 0.043 |
| Research integrity | 0.025 | 0.040 |
| Insufficient payload (model declined to judge) | 0.009 | 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".