Paving the Way to Change for Youth at the Gap between Child and Adolescent and Adult Mental Health Services
Bibliographic record
Abstract
By 2020 mental illness will be one of the 5 most common illnesses causing morbidity, mortality and disability among youth. At least 20% of Canadian youth have a psychiatric disorder the impact of which can dramatically alter their life trajectory. Focus on the factors contributing to this problem is crucial. Lack of coordination between child and adolescent mental health systems (CAMHS) and adult mental health systems (AMHS) and consequent disruption of care during this vulnerable time of transition is one such factor. Reasons for and the impact of this divide are multilayered, many of which are embedded in outdated, poorly informed approaches to care for this population in transition. This paper considers the etiology behind these reasons as potential foci for change. The paper also briefly outlines recent initiatives ongoing in Canada and internationally that reflect appreciation of these factors in the attempt to minimize the gap in service provision for youth in transition. The need to continue with research and program development endeavours for youth with mental illness whereby access to services and readiness for transition is no longer determined by age is strongly supported.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".