Strengthening Strategies For Preventing Drug Abuse In Adolescents And Youth: A Holistic And Comprehensive Approach Based On Social Environment Interventions
Bibliographic record
Abstract
The stagnation of the prevalence of drug abuse among adolescents and youth in Indonesia is a strategic issue that deserves serious attention, especially in the midst of the momentum of the demographic bonus towards a Golden Indonesia 2045. This phenomenon has the potential to have multidimensional impacts, ranging from a decrease in the quality of human resources, a decrease in productivity, to an inhibition of national competitiveness. The analysis shows that the main problem lies in the high risk behaviors among adolescents and youth—such as smoking, alcohol consumption, and nighttime activities—which are rooted in weak self-regulation skills due to a lack of social environmental support. This condition creates a recurrent vulnerability that maintains a stagnation in the prevalence of drug abuse in this age group. Meanwhile, policy support has been available through various instruments, such as the P4GN National Action Plan, Guidance and Counseling services in schools, Youth Care Health Services (PKPR), and the Generation Planning (GenRe) program. However, the effectiveness of the program is still limited because it has not touched the root of vulnerability thoroughly; Preventive interventions for adolescents and youth are fragmented and poorly coordinated, so the impact is not optimal. Therefore, this paper was prepared with the aim of formulating comprehensive and holistic prevention policy recommendations, based on a multi-level approach of social ecology, to significantly reduce the prevalence of drug abuse in adolescents and youth and build the foundation of sustainable prevention.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".