Comparing key characteristics of young adult crack users in and out-of-treatment in Rio de Janeiro, Brazil
Bibliographic record
Abstract
BACKGROUND: Crack use is prevalent among street drug users in Brazilian cities, yet despite recent help system reforms and investments, treatment utilization is low. Other studies have identified a variety of - often inconsistent - factors associated with treatment status among crack or other drug users. This study compared socio-economic, drug use, health and service use characteristics between samples of young adult crack users in- and out-of-treatment in Rio de Janeiro, Brazil. FINDINGS: Street-involved crack users (n = 81) were recruited by community-based methods, and privately assessed by way of an anonymous interviewer-administered questionnaire as well as biological methods, following informed consent. In-treatment users (n = 30) were recruited from a public service in-patient treatment facility and assessed based on the same protocol. Key indicators of interest were statistically cross-compared. Not-in-treatment users were less likely to: be white, educated, stably housed, to be involved in drug dealing, to report lifetime marijuana and current alcohol use, to report low mental health status and general health or addiction/mental health care; they were more likely to: be involved in begging and utilize social services, compared to the in-treatment sample (statistical significance for differences set at p < .05). CONCLUSIONS: In-treatment and not-in-treatment crack users differed on several key characteristics. Overall, in-treatment users appeared to be more socio-economically integrated and connected to the health system, yet not acutely needier in terms of health or drug problems. Given overall low treatment utilization but high need, efforts are required to facilitate improved treatment access and use for marginalized crack users in Brazil.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".