Evaluation of supervised injection facilities as an ethically sound approach to treatment of injection drug abuse
Bibliographic record
Abstract
Novel approaches are needed to address the issue of injection drug use in Canada, which can have negative consequences for drug users and society. Supervised injection facilities (SIFs) are legally sanctioned facilities in Canada where drug users can receive sterile drug paraphernalia, referral to cessation programs and timely medical care if necessary. SIFs operate under the principle of harm reduction, which aims to reduce rates of infection and death due to overdose among drug users. SIFs are largely driven by the utilitarian ideal of maximizing benefit for the greatest number of people, through supervision of active drug users and appropriate referral for those wishing to quit. Deontological theory may support SIFs depending on how one applies the categorical imperative. Studies of the first SIF in North America, Insite, have shown demonstrable reductions in adverse health and societal consequences of injection drug use, rationalizing their implementation under consequentialism. SIFs are, therefore, suitable for greater adoption by the healthcare system.
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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 | 0.115 | 0.211 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".