“Drugs don’t have age limits”: The challenge of setting age restrictions for supervised injection facilities
Bibliographic record
Abstract
Aims: People under age 18 who inject drugs represent a population at risk of health and social harms. Age restrictions at harm reduction programmes often formally exclude this population, but the reason behind such restrictions is lacking in the literature. To help fill this gap, we examine the perspectives of people who use drugs and various other stakeholders regarding whether supervised injection facilities (SIFs) should have age restrictions. Methods: Interviews and focus groups were conducted with a total of 95 people who use drugs and 141 other stakeholders (including police, fire and emergency services personnel, other city employees and officials, healthcare providers, residents and business representatives) in two Canadian cities without SIFs. Findings: We highlight the following thematic areas: mixed opinions regarding specific age restrictions; safety as a priority; different experiences and understandings of youth, agency and drug use; and ideas regarding maturity, “help” and other approaches. We note throughout that a familiar vulnerability–agency dichotomy often surfaced in the discussions. Conclusions: This paper contributes new empirical insights regarding youth access to SIFs. We offer considerations that may inform discussions occurring in other jurisdictions debating SIF implementation and may help remove or clarify age-related policies for harm reduction programmes.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".