Substance use among street children in the city of Yaounde, Cameroon
Bibliographic record
Abstract
Abstract Background Substance use is known to be more common among street children. Sometimes responsible for the runaway and repeated run-away behavior. To be able to reinsert these children, the reasons why there joined the streets, why they use substance and their pattern of substance use need to be understood. Methods We conducted a descriptive cross-sectional mix method study in February 2021 in the streets of Yaounde. We did a semi-structured interview of 159 street children using a sociodemographic questionnaire made of open questions and, the Alcohol, Smoking, and Substance Involvement Screening Test, version 3.0. The data were analyzed using R 4.1.0 for Windows. Results All street children were male. The most common reason for joining the streets was, questing for money, reported by one-quarter of the children. 60% of them used a substance, of which half used a substance because of peer pressure. The most commonly used substance was cannabis (36.48%), followed by Tobacco (35.85%). 14.47% were dependent on tobacco and 11.32% on cannabis. Conclusion Substance use and substance use disorders are highly common among street children of Yaounde. This needs to be addressed to facilitate their reinsertion as shown in other studies. The mechanisms that lead to the absences of girls in the streets should also be explored to see if they can be applied to boys.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".