SUPPORT FOR YOUTH LEAVING CARE: A NATIONAL RESEARCH STUDY, INDIA
Bibliographic record
Abstract
This paper is a condensed version of a study entitled “Beyond 18: Leaving Child Care Institutions — A Study of Aftercare Practices in Five States of India”, conducted and published in 2019 by Udayan Care, a charitable organisation, with support from UNICEF India and Tata Trusts. This research involved the participation of care leavers, government functionaries, duty-bearers, and civil society practitioners. It found that upon turning 18, youth transitioning out of child care institutions to independent life in India experience many challenges, such as securing housing and identity documents; accessing education, skill development, and employment opportunities; and garnering psychosocial support. This study also showed that absent or inadequate aftercare support during transition increases care leavers’ vulnerabilities to homelessness, unemployment, substance misuse, and ruptured social relationships. It also found that continued aftercare support is necessary to foster independent living skills in these young people and enable their reintegration into mainstream society. While exploring the continuum from child care to aftercare, the researchers developed the concept of a “Sphere of Aftercare”, comprising eight domains of support that are considered essential for a successful transition. The study revealed a lack of transition planning at the level of child care institutions and functionaries and a general lack of understanding of the holistic aftercare needs of youth throughout the eight identified domains. The study also found an absence of clarity about stakeholders’ roles; a lack of data management with regard to the number of youth leaving care, leading to inadequate budget planning; and a lack of adequate monitoring mechanisms to assess care leavers’ outcomes. In light of this study’s findings, policy reforms and ways of developing robust aftercare programmes are recommended in relation to policy, practice, and law.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".