Inability to access addiction treatment and risk of HIV infection among injection drug users recruited from a supervised injection facility
Bibliographic record
Abstract
BACKGROUND: Treatment for drug addiction is effective in reducing the harms of injection drug use, including infection with HIV and/or hepatitis C. We sought to examine the prevalence and correlates of being unable to access addiction treatment in a representative sample of injection drug users randomly recruited from a supervised injection facility. METHODS: Using generalized estimating equations, we determined the prevalence and factors associated with being unable to access addiction treatment. RESULTS: Between 1 July 2004 and 30 June 2006, 889 individuals completed at least one interview and were included in this analysis. At each interview, approximately 20% of respondents reported trying but being unable to access any type of drug or alcohol treatment in the previous 6 months. Being unable to access treatment was independently associated with recent incarceration, daily use of heroin and borrowing used syringes. In a secondary question, the majority of individuals reported waiting lists were the reason for being unable to access treatment. CONCLUSION: Given the independent association between inability to access addiction treatment and elevated HIV risk behavior, these results suggest expanding addiction treatment may contribute significantly to HIV prevention efforts in this population.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".