Ambivalence about supervised injection facilities among community stakeholders
Bibliographic record
Abstract
BACKGROUND: Community stakeholders express a range of opinions about supervised injection facilities (SIFs). We sought to identify reasons for ambivalence about SIFs amongst community stakeholders in two Canadian cities. FINDINGS: We used purposive sampling methods to recruit various stakeholder representatives (n = 141) for key informant interviews or focus group discussions. Data were analyzed using a thematic process. We identified seven reasons for ambivalence about SIFs: lack of personal knowledge of evidence about SIFs; concern that SIF goals are too narrow and the need for a comprehensive response to drug use; uncertainty that the community drug problem is large enough to warrant a SIF(s); the need to know more about the "right" places to locate a SIF(s) to avoid damaging communities or businesses; worry that a SIF(s) will renew problems that existed prior to gentrification; concern that resources for drug use prevention and treatment efforts will be diverted to pay for a SIF(s); and concern that SIF implementation must include evaluation, community consultation, and an explicit commitment to discontinue a SIF(s) in the event of adverse outcomes. CONCLUSIONS: Stakeholders desire evidence about potential SIF impacts relevant to local contexts and that addresses perceived potential harms. Stakeholders would also like to see SIFs situated within a comprehensive response to drug use. Future research should determine the relative importance of these concerns and optimal approaches to address them to help guide decision-making about SIFs.
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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 | 0.022 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.014 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".