Evaluation of the outcomes and influencing factors of non-custodial educational programmes for people who use drugs in South Korea
Bibliographic record
Abstract
Abstract This study evaluated the effectiveness of deferred prosecution (DP) and probation, defined as non-custodial education programmes (NCEPs), for drug abusers in South Korea. We analysed participants’ questionnaire responses in the DP (N = 203) and probation (N = 254) groups over a two-year period (January 2022–December 2023). The Wilcoxon signed-rank test was used to compare pre-and post-programme questionnaires on knowledge of drug addiction and the Hanil Drug Insight Scale (HDIS) for participants in DP and probation. Using multivariate logistic analysis, we examined the factors influencing effectiveness, including previous treatment experience, Meaning of Life Questionnaire (MLQ) scores, and mental health status. NCEP effectiveness was assessed based on the participants’ reported intentions for future treatment at the end of the NCEP. DP participants and probationers showed significant increases in their knowledge of drug addiction and HDIS scores (p < 0.001). Results showed that treatment experience (odds ratio [OR] 3.73, 95% confidence interval [CI] 1.30–10.71), poor mental health (OR 2.45, 95% CI 1.01–5.95), and good MLQ (OR 2.90, 95% CI 1.50–5.63) were significantly associated with improved NCEP effectiveness. This study provides the first evidence of the beneficial outcomes of the NCEP and identifies the factors influencing its effectiveness.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".