The effect of a low intensity intervention on the wellbeing of children in the juvenile justice system in India: Results from a pilot study
Bibliographic record
Abstract
OBJECTIVE: Children in the Juvenile Justice System (JJS) face multiple adversities that may predispose them to developing mental health (MH) problems. Interventions that enhance the MH and well-being of these children while providing access to appropriate MH care are needed. This study examines the effect of a multipronged, low-intensity intervention on adolescents' well-being, stress levels, MH knowledge, and stigma perceptions in Childcare Institutions (CCIs) across Odisha and Tamil Nadu, India. METHODS: Implemented in 7 CCIs, the intervention aimed to enhance the MH Literacy (MHL) and well-being of children in the CCIs through the Youth-Friendly Spaces (YFS) using experiential methods. It also sought to improve the MHL of the staff and administration within the JJS and provide access to MH care by linking CCIs to available local resources. Information using validated scales was obtained at baseline and six months. RESULTS: Only 180 of the 310 children for whom baseline data was available remained in the CCI at six months. A statistically significant improvement in well-being (34.5 +13.7-55.9 +12.2, p < 0.001), stress (18.5 +6.8-17 +6.1, p = 0.044) and stigma (32 +11.9-29.4 +11.4, p = 0.023) scores were observed. Adjusted linear regression analysis showed significant differences across genders, sites, and child types. DISCUSSION: This intervention, comprising YFS creation, MHL enhancement, and referral system development, is significantly associated with improved well-being of children in CCIs. The results underscore the need for tailored interventions based on gender, location, and child type. The study highlights the potential scalability of such programs in resource-constrained settings for vulnerable children.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".