Risk Factors for Narcotic Use in Street Children: A Cross-Sectional Analysis From a Low-Middle-Income Country
Bibliographic record
Abstract
Worldwide, indulgence in high-risk behaviors such as substance abuse is on the rise in street children. Though substance abuse among street children has been investigated and reported in Pakistan, few studies have explored the relationship between narcotic use and its associated factors. This study was conducted to determine factors associated with narcotic use among street children in Islamabad Capital Territory. An analytical cross-sectional survey of a probability-based sample of 443 (males) street children aged 12 to 18 years, was conducted in Islamabad in March 2022. Using self-reported measures, the relationship between narcotic use and associated factors was determined using multivariate regression analysis. Out of 443 street children, with a mean age of 16.3 ± 1.6 years, 244 (55%) were between 17 and 18 years old. 119 (26.9%) worked as garbage collectors and 76 (17.2%) worked as car washers. The most common substance used was cigarettes in 285 (64.3%), naswaar in 172 (38.8%), hashish in 144 (32.5%), and alcohol in 63 (14.2%) street children. There were 164 (37%) street children who admitted having used narcotics (hashish, heroin, and bhang). On multivariate analysis, age > 16 years (OR: 2.3), sleeping on the streets (OR: 2.4), higher monthly income > Rs.18,000 (OR: 1.6), use of drugs by friends (OR: 5), and involvement in the selling of drugs (OR: 10.3) were independently associated with narcotic use. Substance abuse is a concerning trend among street children in Islamabad. When certain high-risk factors are present, these children are prone to narcotic use.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.000 | 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 teacher head, 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".