Family determinants of substance use among Rwandan youths: a case study of Gitagata Rehabilitation Centre
Bibliographic record
Abstract
Background Substance use remains a public health threat with serious effects. It may be predicted by complex factors ranging from family to psychosocial disparities. We examined the magnitude of substance use and its influence on families among adolescents from Gitagata Rehabilitation Center.Methods A cross-sectional study of 57 adolescents was conducted.Results Our findings revealed that the proportion of substance users was higher (42 of 57 teens) than the proportion of non-substance users. Socio-demographic influences such as occupation, age and residence significantly increased the odds of substance use at p < .05. Adolescents who experienced domestic violence were more likely to be substance users [OR = 3.56; 95%CI (1.12–11.3)] than those who did not experience domestic violence. Children who were not neglected by their families were less likely to become substance users than neglected children [OR = 0.05; 95% CI (0.01–0.29)]. Adolescents from low-functioning families were more likely to be substance users [OR = 5.18; 95% CI (1.58–16.95)] than those from proper-functioning families. Those who were not properly monitored by their parents were more likely to use drugs [OR = 5.62; 95% CI (2.52–12.5)].Conclusion A multidisciplinary team is required to step up efforts to inform parents about their responsibility in lowering the likelihood that their children may use drugs by enhancing parental monitoring.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".