Facing the Challenge of Care for Child and Youth Mental Health in Canada: A Critical Commentary, Five Suggestions for Change and a Call to Action
Bibliographic record
Abstract
Neuropsychiatric disorders contribute most to the global burden of disease in young people (World Health Organization [WHO] 2003), approaching about 30% of the total global disease burden in those aged 10-19 years. Comparative data are not available for Canada, but the proportional burden of mental disorders in Canadian youth would be expected to be higher as our rates of human immunodeficiency virus/acquired immunodeficiency syndrome, tuberculosis, malaria and iron-deficiency disorders are substantially less than those in low-income countries. National estimates identify that about 15% of Canadian young people suffer from a mental disorder, but only about one in five of those who require professional mental health care actually receive it (Government of Canada 2006; Health Canada 2002; Kirby and Keon 2006; McEwan et al. 2007; Waddell and Shepherd 2002). And recent reports suggest that the human fallout from this reality may go beyond the well-known negative impacts of early-onset mental disorders on social, interpersonal, vocational and economic outcomes. For example, rates of mental disorder are very high in incarcerated youth, suggesting that, for some, jails are becoming the home for mentally ill young people (Kutcher and McDougall 2009).
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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 | 0.017 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.019 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.011 | 0.005 |
| Research integrity | 0.048 | 0.081 |
| 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".