Females Experiencing Sexual and Drug Vulnerabilities Are at Elevated Risk for HIV Infection Among Youth Who Use Injection Drugs
Bibliographic record
Abstract
OBJECTIVES: To compare sociodemographic, drug-related, and sexual risk variables between young (13-24 years of age) and older (> or =25 years of age) injection drug users (IDUs); and to determine HIV prevalence and associated risk factors for HIV infection among young IDUs. METHODS: Data were collected through the Vancouver Injection Drug Users Study (VIDUS). To date, over 1400 Vancouver area IDUs have been enrolled and observed during follow-up. Sociodemographic, drug-related, and sexual risk variables were compared between younger and older IDUs using nonparametric methods. Mantel-Haenszel and logistic regression methods were used to compare HIV-positive and HIV-negative female youth. RESULTS: Younger injectors (N = 232) were more likely to be female; work in the sex trade; report condom use; inject heroin daily; smoke crack cocaine daily; and need help injecting. HIV prevalence at baseline among the youth was 10%. HIV prevalence was associated with female gender; history of sexual abuse; engaging in survival sex; injecting heroin daily; injecting speedballs (a mixture of heroin and cocaine) daily; and having numerous lifetime sexual partners. CONCLUSION: Our data show that HIV positivity among young IDUs is concentrated among females engaged in dual sexual and drug-related risk exposure categories. Over half the HIV-positive youth were Aboriginal (a classification used by the federal government in Canada to include native peoples of all ethnic groups). Targeted interventions that take into account sexual and drug risk for young female and Aboriginal peoples are urgently needed.
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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 | 0.000 | 0.000 |
| 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".