Flawed reports can harm: the case of supervised consumption services in Alberta
Bibliographic record
Abstract
Supervised consumption services have been scaled up within Canada and internationally as an ethical imperative in the context of a public health emergency. A large body of peer-reviewed evidence demonstrates that these services prevent poisoning deaths, reduce infectious disease transmission risk behaviour, and facilitate clients' connections to other health and social services. In 2019, the Alberta government commissioned a review of the socioeconomic impacts of seven supervised consumption services in the province. The report is formatted to appear as an objective, scientifically credible evaluation of these services; however, it is fundamentally methodologically flawed, with a high risk of biases that critically undermine its authors' assessment of the scientific evidence. The report's findings have been used to justify decisions that jeopardize the health and well-being of people who use drugs both in Canada and internationally. Governments must ensure that future assessments of supervised consumption services and other public health measures to address drug poisoning deaths are scientifically sound and methodologically rigorous. Health policy must be based on the best available evidence, protect the right of structurally vulnerable populations to access healthcare, and not be contingent on favourable public opinion or prevailing political ideology.
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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 | 0.050 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.026 | 0.016 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.016 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".