Rate of detoxification service use and its impact among a cohort of supervised injecting facility users
Bibliographic record
Abstract
BACKGROUND: Vancouver, Canada recently opened a medically supervised injecting facility (SIF) where injection drug users (IDU) can inject pre-obtained illicit drugs. Critics suggest that the facility does not help IDU to reduce their drug use. METHODS: We conducted retrospective and prospective database linkages with residential detoxification facilities and used generalized estimating equation (GEE) methods to examine the rate of detoxification service use among SIF participants in the year before versus the year after the SIF opened. In secondary analyses, we used Cox regression to examine if having been enrolled in detoxification was associated with enrolling in methadone or other forms of addiction treatment. We also evaluated the impact of detoxification use on the frequency of SIF use. RESULTS: Among 1031 IDU, there was a statistically significant increase in the uptake of detoxification services the year after the SIF opened. [odds ratio: 1.32 (95% CI, 1.11-1.58); P = 0.002]. In turn, detoxification was associated independently with elevated rates of methadone initiation [relative hazard = 1.56 (95% CI, 1.04-2.34); P = 0.031] and elevated initiation of other addiction treatment [relative hazard = 3.73 (95% CI, 2.57-5.39); P < 0.001]. Use of the SIF declined when the rate of SIF use in the month before enrolment into detoxification was compared to the rate of SIF use in the month after discharge (24 visits versus 19 visits; P = 0.002). CONCLUSIONS: The SIF's opening was associated independently with a 30% increase in detoxification service use, and this behaviour was associated with increased rates of long-term addiction treatment initiation and reduced injecting at the SIF.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".