Drug consumption rooms: A systematic review of evaluation methodologies
Bibliographic record
Abstract
ISSUES: Drug consumptions rooms (DCR) and supervised injecting facilities (SIF) are expanding internationally. Previous reviews have not systematically addressed evaluation methodologies. APPROACH: Results from systematic searches of scientific databases in English until June 2017 were coded for paper type, country and year of publication. For evaluation papers, study outcome, methodology/study design and main indicators of DCR/SIF 'exposure' were recorded. KEY FINDINGS: Two hundred and nineteen eligible peer-reviewed papers were published since 1999: the majority from Canada (n = 117 papers), Europe (n = 36) and Australia (n = 32). Fifty-six papers reported evaluation outcomes. Ecological study designs (n = 10) were used to assess the impact on overdose, public nuisance and crime; modelling techniques (n = 6) estimated impact on blood-borne diseases, overdose deaths and costs. Papers using individual-level data included four prospective cohorts (n = 28), cross-sectional surveys (n = 7) and service records (n = 5). Individual-level data were used to assess safer injecting practice, uptake into health and social services and all the other above outcomes except for impact on crime and costs. Four different indicators of DCR/SIF attendance were used to measure service 'exposure'. IMPLICATIONS: Research around DCRs/SIFs has used ecological, modelling, cross-sectional and cohort study designs. Further research could involve systematic inclusion of a control group of people who are eligible but do not access SIFs, validation of self-reported proportion of injections at SIFs or a stepped-wedge or a cluster trial comparing localities. CONCLUSIONS: Methodologies appropriate for DCR/SIF evaluation have been established and can be readily replicated from the existing literature. Research on operational aspects, implementation and transferability is also warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".