The transition from state care to adulthood: International examples of best practices
Bibliographic record
Abstract
Around the world, the transition from adulthood is a difficult time for many youth. It is even more difficult for those who are transitioning to adulthood without the benefit of a support network full of family and friends. Youth leaving state care face a transition to independence and adulthood without many of the skills and supports most others take for granted. Preparedness is key to a successful transition, and youth leaving state care tend to be lacking it. In order for youth to truly be prepared for the transition process, they must have support in key areas of their lives: relationships, education, housing, life skills, identity, youth engagement, emotional healing, and adequate financial support. Without these supports, the dismal outcomes for youth transitioning to adulthood will remain unchanged. The United States, England, and Australia have successful programs targeting youth as they transition out of state care. These initiatives bring together and address the variety of needs of this unique population and aim to improve outcomes. While many of these programs and policies are in their infancy, they show promising results, and each contributes valuable experience to successfully working with youth through this tough transition.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".