Safer Injection Facilities in North America: Their Place in Public Policy and Health Initiatives
Bibliographic record
Abstract
The continuing threat posed by HIV, HCV, drug overdose, and other injection-related health problems in both the United States and Canada indicates the need for further development of innovative interventions for drug injectors, for reducing disease and mortality rates, and for enrolling injectors into drug treatment and other health care programs. Governmentally sanctioned “safer injection facilities” (SIFs) are a service that many countries around the world have added to the array of public health programs they offer injectors. In addition to needle exchange programs, street-outreach and other services, SIFs are clearly additions to much larger comprehensive public health initiatives that municipalities pursue in many countries. A survey of the existing research literature, plus the authors' ethnographic observations of 18 SIFs operating in western Europe and one SIF that was recently opened in Sydney, Australia, suggest that SIFs target several problems that needle exchange, street-outreach, and other conventional services fall short in addressing: (1) reducing rates of drug injection and related-risks in public spaces; (2) placing injectors in more direct and timely contact with medical care, drug treatment, counseling, and other social services; (3) reducing the volume of injectors' discarded litter in, and expropriation of, public spaces. In light of the evidence, the time has come for more municipalities within North America to begin considering the place of SIFs in public policy and health initiatives, and to provide support for controlled field trials and demonstration projects of SIFs operating in injection drug-using communities.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".