Promoting pedagogical ‘safe supply’ in response to Canada’s toxic-drug-poisoning crisis: a commentary on substance use education in social work curricula and the ‘toxic’ nature of Canada’s current drug supply
Bibliographic record
Abstract
North America has witnessed an unprecedented level of devastation wrought by the toxic-drug-poisoning crisis – otherwise referred to as the ‘opioid’ or ‘overdose’ epidemic – with Canada witnessing over 47,000 fatalities from January 2016 to March 2024. Structured by a series of propositions, this article critically interrogates the conspicuous under-representation of harm reduction and substance use education in social work curricula across Canada, a country that has similarly seen skyrocketing mortality rates resulting from the displacement of heroin with incredibly strong and dangerous synthetic opioid analogues like fentanyl. Given the crucial role that social workers could play in ameliorating the impact of Canada’s toxic, tainted drug supply, this commentary thus asserts that in light of current conditions, it is imperative for social work regulatory bodies – namely, the national Canadian Association of Social Workers – to begin explicitly embracing and promoting the principles of harm reduction in an effort to better equip social workers for direct, equitable engagement with people who use drugs.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.032 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.043 | 0.053 |
| Insufficient payload (model declined to judge) | 0.004 | 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".