Overview of Ontario's Screening and Outcome Measurement Initiative in Children's Mental Health.
Bibliographic record
Abstract
INTRODUCTION: Ontario's mental health practitioners strive to provide the best services for the most children and youth in the face of limited resources and increasing demand. METHOD: To do this efficiently and ethically necessitates identifying those at greatest risk, determining which services are most effective for a variety of children, and demonstrating improved functioning post-treatment. Standardized screening can assist in triaging those at greatest risk and outcome measurement can demonstrate improvement and treatment effectiveness. RESULTS: To this end, Ontario has initiated systematic screening and outcome measurement for children ages 6 to 17 years receiving mental health services in selected hospital-based and community organizations. CONCLUSION: Standardized screening and outcome tools are key building blocks for improving the quality of service and promoting the use of evidence-based practices across the system. The lessons learned to date suggest there is a need to build individual and organizational readiness for change, to improve the state of technological literacy and infrastructure across the sector, and to improve the exchange of knowledge among stakeholders regarding the clinical benefits of the toolsand the data they will produce regarding the state of children and youth receiving mental health service in Ontario.
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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 | 0.025 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".