Examining the experiences of justice-involved youth with \nmental health and substance use needs
Bibliographic record
Abstract
Awareness surrounding the impact of psychological/mental health and substance use needs among young people involved in the justice system has increased in recent years. The life trajectories of youth who have justice system involvement are plagued with inequality created by various structural and social factors. Of these, mental health and substance use are among the most commonly reported. Youth who experience potentially traumatic events and are exposed to adverse events early in life, particularly childhood, have been found to experience greater psychological/mental health and substance use needs, and increased justice system involvement (Felitti et al., 1998, 2002; Abram et al, 2004; Baglivio et al., 2014, 2020). The present study relied on data from the case files of 192 youth probationers from Western Canada who were classified as “serious/violent”, to explore their mental health and substance use behaviours. Findings are discussed with regard to how justice-involved youth with “high/specialized” mental health and/or substance use needs specifically, have unique experiences of mental health and substance use compared to other justice-involved youth. Results demonstrate the importance of examining how trauma and adverse experiences in early childhood and youth affect specific psychological responses/health-related issues and higher-level substance use.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".