Youth aging out of care and contact with the criminal justice system: the role of educational transitions in early adulthood
Bibliographic record
Abstract
The process of transitioning of placement is marked by difficult transitions and is complicated by a history of placement experiences. Studies show that most young people who age out of placement care are enrolled in school just before they leave, but this proportion drops drastically soon after they leave care. This transition has the potential to increase the risk of being involved in the justice system. Conversely, staying in school after placement has the potential to prevent contact with the justice system. To examine whether educational transitions during the process of leaving placement care in early adulthood influence the risk of justice system involvement. More precisely, the study focuses on whether leaving school increases this risk among youth aging out of care. It also uses moderation analyses to assess whether this association varies based on placement experiences, such as placement instability and group placement. We used a subsample of the EDJeP study from Québec, Canada, consisting of youths who participated in the third wave and who were in school before leaving placement (n = 413). Administrative data from youth protection services and data from the three waves of questionnaires were analyzed. We used maximum likelihood logistic regression models to predict justice system involvement during early adulthood as a function of leaving school. Interaction terms were used to determine whether moderation effects were present. The results show that young people who leave school when they age out of placement are at greater risk of being involved in the justice system during early adulthood (OR = 4.55, p < 0.001). Conversely, young people who stayed in school after aging out of care were less likely to be involved in the justice system during early adulthood. However, there were no significant moderation effects (p > 0.05) with the placement experiences. • Among youth enrolled in school before leaving care, our results suggest that a significant proportion (64 %) will leave school when they age out of care. • Compared with young people who stayed in school after aging out of care, those who left school were almost five times more likely to report being involved with the adult justice system in wave 3. • Our results highlight the need for policies and programs that support education among care leavers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".