Examining a migration-based phenomenon of heroin use in an urban drug scene in Sao Paulo, Brazil
Bibliographic record
Abstract
Purpose Brazil’s street-based drug use is mostly characterized by non-injection psychostimulant (e.g. crack-cocaine) drug use in Brazil, with limited interventions and service availability. Recently, an influx of multi-ethnic migrants within an urban drug scene in Sao Paulo was associated with heroin use, a drug normatively absent from Brazil. The purpose of this paper is to characterize and compare heroin use-related characteristics and outcomes for an attending sub-sample of clients from a large community-based treatment centre (“CRATOD”) serving Sao Paulo’s local urban drug scene. Design/methodology/approach All non-Brazilian patients (n= 109) receiving services at CRATOD for 2013–2016 were identified from patient files, divided into heroin users (n= 40) and non-heroin users (n= 69). Based on chart reviews, select socio-demographic, drug use and health status (including blood-borne-virus and other infections per rapid test methods) were examined and bi-variately compared. Multi-variate analyses examined factors independently associated with heroin use. Findings Most participants were male and middle-aged, poly-drug users and socio-economically marginalized. While heroin users primarily originated from Africa, they reported significantly more criminal histories, drug (e.g. injection) and sex-risk behaviors and elevated rates of BBV (e.g. Hepatitis C Virus and HIV). A minority of heroin users attending the clinic was provided methadone treatment, mostly for detoxification. Originality/value This study documented information on a distinct sample of mostly migration-based heroin users in Sao Paulo, Brazil. Based on the local experience, global migration dynamics can bring changes to established drug use cultures and services, including new challenges for drug use-related related behaviors and therapeutic interventions that require effective understanding and addressing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".