Legal Systems Involvement and Mental Health: The Challenges for Young People Engaged in the Family Law and Criminal Justice Systems
Bibliographic record
Abstract
Young people who are engaged with the child welfare and youth criminal justice systems are being severely harmed by their involvement with these and other legal systems. This crisis is particularly pronounced for young people who experience mental health issues. A significant majority of the young people engaged with the child welfare and youth criminal justice systems experience mental health issues, yet despite their engagement with state systems mandated to protect them and promote their well-being, few receive the supports and services they need. In this dissertation I explore the mutually constitutive role of legal rules in the child welfare, youth criminal justice, civil mental health, and education systems; particularly how these rules, through their interpretation and implementation in practice, create, contribute to, and/or compound the barriers to service use experienced by young people with mental health issues. Using legal doctrinal analysis, original empirical data gathered through qualitative semi-structured interviews with legal and mental health professionals, scholarly literature, and secondary sources which relay the lived experiences of these young people, I illuminate how, in practice, the complex, intersecting challenges they experience are more often compounded, exacerbated, and multiplied rather than ameliorated, by their engagement with the state systems mandated to protect them and promote their well-being. I discuss how this systemic failure results in severe short- and long-term difficulties and outcomes for the young people involved; and how it comes with significant costs and consequences for these young people, their families, and society more generally. I then examine and analyze the reforms needed to overcome the barriers to service use experienced by these young people and to facilitate their access to and engagement with needed supports and services. I recommend specific changes to legislation, policies and practices, funding, and service delivery and discuss how these changes could curb the harms currently experienced by young people with legal systems involvement. Finally, I consider the significant investments required to develop and implement these changes as well as the human and financial costs of continuing the state’s failure to meet the needs of this underserved and often overlooked population of young people.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".