Correlates of duration between initial drug use and first drug injection among people who inject drugs in Iran, 2020
Bibliographic record
Abstract
BACKGROUND: People who use non-injection drug use are at risk of transitioning to injecting drugs, which increases their vulnerability to HIV and other blood-borne infections. This study aimed to investigate the correlates of the duration between the first drug use and the first drug injection among people who inject drugs (PWID) in Iran, as well as the reasons for injection initiation. METHODS: We analyzed data from the fourth national bio-behavioral surveillance survey among PWID in Iran, conducted in 2020 across 11 cities using respondent-driven sampling (n = 2,684). A generalized linear mixed model with a gamma-distributed dependent variable and log link function was used to investigate the correlates of transition time from non-injection to injection drug use. RESULTS: Among 2,356 participants included in the analysis, the mean ± SD of the duration between the first drug use and the first drug injection was 9.37 ± 6.8 years. Factors associated with earlier injection initiation included: age under 30 years (p-value < 0.001), being single (p-value < 0.001) or divorced/widowed (p-value = 0.007), history of incarceration (p-value = 0.001), sexual debut before age 18 (p-value < 0.001), and history of depression (p-value < 0.001). Peer influence (665;29.1%) and pleasure-seeking behavior (534; 23.3%) were the most common motives for injection initiation. CONCLUSIONS: The transition to injection drug use among PWID in Iran often occurs within a decade of initial drug use and is influenced by demographic, social, and psychological factors. Prevention strategies should focus on early intervention for at-risk youth, address mental health needs, and leverage peer influence. Policymakers should prioritize evidence-based, multi-faceted approaches that target both individual and structural factors to delay or prevent the transition to injection drug use and reduce associated health risks.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".