The Damage Done: How the Disease Model of Addiction Harms Marginalized People in Canada
Bibliographic record
Abstract
Over 50,000 Canadians have died of unregulated drug poisoning since 2016. And while the toxic drug crisis is the leading cause of death in British Columbia for people between the ages of 10 and 59, some British Columbians are at a higher risk than others. In 2023, First Nations people—who comprise 3.4% of BC’s population—accounted for 17.8% of its toxic drug deaths. Other marginalized communities are also at heightened risk: racialized people, young people, and people living in poverty or without shelter are all disproportionately impacted. These disparities reflect more than a public health crisis—they largely exist due to legal frameworks and policy choices that continue to pathologize and punish substance use, especially among marginalized communities. This Article argues that the prevailing “disease model” of addiction has not disrupted punitive approaches to drug policy and has instead justified coercive legal interventions designed to treat the “disease of addiction.” While harm reduction is nominally a pillar of Canada’s drug policy, popular reforms like drug treatment courts and involuntary substance use treatment remain grounded in the disease model’s approach to the conceptualization and treatment of addiction, an approach that is inconsistent with harm reduction principals and fails to address the structural conditions that contribute to drug-related harms. Meaningful drug policy reform requires more than a simple shift from criminalization to medicalization; a true harm reduction framework must approach problematic drug use as a complex social and public health issue and not simply as a disease to be treated.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.046 | 0.017 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".