Exploring sex differences in drug use, health and service use characteristics among young urban crack users in Brazil
Bibliographic record
Abstract
INTRODUCTION: Studies have shown important gender differences among drug (including crack) users related to: drug use patterns; health risks and consequences; criminal involvement; and service needs/use. Crack use is prevalent in Brazil; however, few comparative data by sex exist. We examined and compared by sex key drug use, health, socio-economic indicators and service use in a bi-city sample of young (18-24 years), regular and marginalized crack users in Brazil. METHODS: Study participants (total n = 159; n = 124 males and n = 35 females) were recruited by community-based methods from impoverished neighborhoods in Rio de Janeiro and Salvador. Assessments occurred by an anonymous interviewer-administered questionnaire and serum collection for blood-borne virus testing between November 2010 and June 2011. Descriptive statistics and differences for key variables by sex were computed; in addition, a 'chi-squared automatic interaction detector' ('CHAID') analysis explored potential primary factors differentiating male and female participants. RESULTS: Most participants were non-white, and had low education and multiple income sources. More women had unstable housing and income from sex work and/or panhandling/begging, whereas more men were employed. Both groups indicated multi-year histories of and frequent daily crack use, but virtually no drug injection histories. Men reported more co-use of other drugs. More women were: involved in sex-for-drug exchanges; blood-borne virus (BBV) tested and HIV+. Both groups reported similar physical and mental health patterns; however women more commonly utilized social or health services. The CHAID analysis identified sex work; paid work; begging/panhandling; as well as physical and mental health status (all at p < 0.05) as primary differentiating factors by sex. CONCLUSIONS: Crack users in our study showed notable differences by sex, including socio-economic indicators, drug co-use patterns, sex risks/work, BBV testing and status, and service utilization. Results emphasize the need for targeted special interventions and services for males and female 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 | 0.000 | 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".