Prevalence of psychoactive substance use among youth in Rwanda
Bibliographic record
Abstract
BACKGROUND: Substance use among youth is a significant public health concern worldwide. However, little is known in Rwanda about the prevalence of drug use among youth. The goal of the current study was to assess the prevalence and determinants of substance use among youth in Rwanda. METHODS: A cross-sectional home survey was carried out with 2479 Rwandan youth. Youth ranging, in age from 14-35 years, were randomly selected from 20 out of the 30 districts in the country. The youth were interviewed using a questionnaire that included socio-demographic information and self-reported substance use. Misuse and dependence on alcohol, marijuana and tobacco were respectively assessed by the Alcohol Use Disorders Identification Test (AUDIT), the Cannabis Abuse Screening Test (CAST), and the Hooked on Nicotine Checklist (HONC). RESULTS: Overall, the prevalence rate of substance use over the month prior to the survey was 34% for alcohol, 8.5% for tobacco smoking, 2.7% for cannabis, 0.2% for glue and 0.1% for drugs such as diazepam. 7.46% (one in thirteen) of the youth were alcohol dependent, 4.88% (one in twenty) were nicotine dependent, and 2.54% (one in forty) dependent on cannabis. CONCLUSIONS: Our findings demonstrate that tobacco, alcohol, marijuana and other substance use are realities in the daily lives of youth in Rwanda. Further research is needed to monitor the evolution of this phenomenon and its determinants and in order to initiate evidenced-based interventions.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".