The Journey of Addiction: Barriers to and Facilitators of Drug Use Cessation among Street Children and Youths in Western Kenya
Bibliographic record
Abstract
This mixed-methods study examined barriers to and facilitators of street children's drug use cessation in Eldoret, Kenya utilizing a cross-sectional survey and focus group discussions with a community-based sample of street-involved children and youth. The primary objective of this study was to describe factors that may assist or impede cessation of drug use that can be utilized in developing substance use interventions for this marginalized population. In 2011, 146 children and youth ages 10-19 years, classified as either children on the street or children of the street were recruited to participate in the cross-sectional survey. Of the 146 children that participated in the survey 40 were invited to participate in focus group discussion; 30 returned voluntarily to participate in the discussions. Several themes were derived from children's narratives that described the barriers to and facilitators of drug cessation. Specifically, our findings reveal the strength of the addiction to inhalants, the dual role that peers and family play in substance use, and how the social, cultural, and economic context influence or impede cessation. Our findings demonstrate the need to integrate community, family and peers into any intervention in addition to traditional medical and psychological models for treatment of substance use dependence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".