Strengthening service integration across the mental health care system: An exploration of service complexity and resource intensity among youth in Ontario
Bibliographic record
Abstract
The prevalence of mental health disorders among young persons in Canada is high. Approximately 1 in 5 will experience a mental health concern. This estimate does not capture individuals who experience disruptive symptoms but fail to meet diagnostic thresholds. In Canada, the provision of mental health supports is fragmented across service sectors and lacks a standardized governance to identify, prioritize and triage youth in need of assistance. This results in a paucity of information about service users, especially as it relates to autistic youth. This three-paper dissertation utilized multi-sectoral assessment tools to describe treatment-seeking\nyouth in Ontario, Canada in terms of service complexity and mental health resource intensity. The first paper examined the association between sex, age, caregiver distress, finance, co-occurring conditions, intellectual disability and evaluated health status on mental health service complexity among autistic youth. Results indicated that older youth, females, several co-occurring conditions, no intellectual disability and longer durations of programming resulted in greater service complexity. The second paper explored classes of health risk behaviours related to alcohol and substance use among youth. Three classes of health risk behaviours emerged: (i) non-use, (ii) cannabis use and (iii) polysubstance use. Additionally, sex, age, polyvictimization, a diagnosis of autism spectrum disorder, family functioning, peer conflict and school engagement were associated with class membership. The third paper examined classes of health risk behaviours specifically among autistic youth and their relationships to mental health state indicators and resource intensity. The results indicated three classes of health risk behaviours: (i) recent smoking and substance use, (ii) non-use and (iii) recent cannabis use. Additionally, levels of sleep difficulties and externalizing symptoms differed between classes, whereas communication difficulties and internalizing symptoms did not. Female youth and recent cannabis users required higher mental health resource intensity compared to male peers and non-users. The dissertation concludes by discussing the implications of the overall findings for research and for mental health policy, specifically as it relates to the integration of supports for youth in Ontario, Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".