Le stanze del consumo. Un luogo sicuro e supervisionato per l’uso di sostanze illecite
Bibliographic record
Abstract
Drug consumption rooms (DCRs) are one of the most innovative and disputed strategies for reducing drug-related harm. DCRs are venues that provide hygienic environments in which people are allowed to use illegal drugs under supervision of a healthcare professional, a trained allied service provider, or a peer. The first trial of this service took place more than thirty years ago and to date there are more than 90 DCRs operating in Oceania, Europe and North America. Although Italy has long-standing experience in harm reduction policies, sanctioned DCRs have been never implemented and, likewise, there are few publications on this topic. Our aim is therefore to present DCRs to the Italian readers, by highlight- ing its purposes, characteristics, evidence-based effectiveness and impacts at the urban level. Moreover, we discuss two case studies: Insite and other experiments that have taken place in Vancouver, a city that stood up as an example of excellence and innovation in this field and the ‘Stanzetta of Collegno’ (Turin), a long-lasting drug user-run DCR experience. Since the integration of harm reduction interventions in the Essential Assistance Levels in 2017, we aim to foster a debate on DCRs as a way to expand Italian harm reduction policies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".