The Global Distribution and Epidemiology of Psychoactive Substance Use and Injection Drug Use Among Street-Involved Children and Youth: A Meta-Analysis
Bibliographic record
Abstract
Background: Globally, street-involved children and youth (SICY) who work and live on/of the streets are at higher risk of increased psychoactive substances and injecting drug use. Objectives: The present study aimed to identify the prevalence, distribution, sociodemographic factors, and risk-taking behaviors associated with psychoactive substances and injecting drug use among SICY. Methods: Studies in English published from December 1 1985 to July 1 2022, were searched for on PubMed, Scopus, Cochrane, and Web of Science to identify primary studies on psychoactive substances and injecting drug use among SICY. The pooled-prevalence estimates were obtained using a robust fixed-effects model. Results: The most commonly reported life-time and current psychoactive substance was tobacco followed by cannabis, LSD/ecstasy, cocaine, methamphetamine, heroin and injection drug use. The results showed that life-time and current prevalence of methamphetamine and cannabis use, as well as life-time prevalence of cocaine, LSD/ecstasy, heroin, tobacco, and injecting drug use increased as age rose while current prevalence of cocaine and tobacco use decreased as age rose. SICY who were male, homeless, had parents who had died, had history of substance use among family members or best friends, had experienced violence, had casual sex partners, had a history of working in the sex trade, and had unprotected sex were all related to psychoactive substance use and injecting drug use. Conclusions: Research examining this population suffers from lack of studies, therefore, improving the knowledge for interventions aimed at reducing risk behaviors, particularly those related to the transmission of sexually transmitted infections such as HIV is of great importance.
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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.058 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".