Factors influencing psychoactive substance use among adolescents in public secondary schools in Uganda
Bibliographic record
Abstract
Rukundo, A., Kibanja, G., & Steffens, K. (2017). Factors influencing psychoactive substance use among adolescents in public secondary schools in Uganda. The International Journal Of Alcohol And Drug Research, 6(1), 69-76. doi:http://dx.doi.org/10.7895/ijadr.v6i1.237Introduction: Studies exploring psychoactive substance use (PASU) among adolescents report a variety of both intrapersonal and interpersonal negatively and positively reinforcing factors. While existing studies have looked at factors affecting PASU among adolescents in general, little has been done to explore such factors in schools.Objective: This paper examines the factors that influence PASU among adolescents in Ugandan public schools.Methods: The study generated data from 12 focus group discussions (FGDs), based on a qualitative, cross-sectional exploratory design using purposive sampling. We used a focus group guide based on the question “What factors influence use ofResults: All focus groups noted peer pressure as the strongest factor influencing use of substances in schools, with relief from domestic stress being identified as the second strongest factor. The FGDs tackled other factors related to PASU in public schools in Uganda, though not to as big of an extent.Conclusion: Present study results generally seem to suggest that PASU among adolescents in public schools is a result of the interaction between adolescents, the substances in question, and the environment in which those adolescents live.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".