Gender influences on initiation of injecting drug use
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Gender differences in illicit drug use patterns and related harms (e.g. HIV infection) are becoming increasingly recognized. However, little research has examined gender differences in risk factors for initiation into injecting drug use. We undertook this study to examine the relationship between gender and risk of injection initiation among street-involved youth and to determine whether risk factors for initiation differed between genders. METHODS: From September 2005 to November 2011, youth were enrolled into the At-Risk Youth Study, a cohort of street-involved youth aged 14-26 in Vancouver, Canada. Cox regression analyses were used to assess variables associated with injection initiation and stratified analyses considered risk factors for injection initiation among male and female participants separately. RESULTS: Among 422 street-involved youth, 133 (32.5%) were female, and 77 individuals initiated injection over study follow-up. Although rates of injection initiation were similar between male and female youth (p = 0.531), stratified analyses demonstrated that, among male youth, risk factors for injection initiation included sex work (Adjusted Hazard Ratio [AHR] = 4.74, 95% Confidence Intervals [CI]: 1.45-15.5) and residence within the city's drug use epicenter (AHR = 1.95, 95% CI: 1.12-3.41), whereas among female youth, non-injection crystal methamphetamine use (AHR = 4.63, 95% CI: 1.89-11.35) was positively associated with subsequent injection initiation. CONCLUSION: Although rates of initiation into injecting drug use were similar for male and female street youth, the risk factors for initiation were distinct. These findings suggest a possible benefit of uniquely tailoring prevention efforts to high-risk males and females.
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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 | 0.002 |
| 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) | 0.005 | 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".