Monitoring the Quality of Children’s Community Mental Healthcare in Ontario: The Development of Pilot Quality Indicators
Bibliographic record
Abstract
In Ontario, one of the primary ways children and youth access mental health services is through community-based agencies. Due to the lack of homogeneity in how these agencies operate and report data, agencies, governments, and policymakers face significant barriers in accurately assessing, monitoring, and comparing the quality of services delivered across the province. This leads to difficulties in macro-level understanding of service delivery quality and the appropriate allocation of resources. A solution to this is the use of quality indicators (QIs) to measure specific components of healthcare quality and the risk adjustment of these QIs to allow for accurate agency comparisons. This pilot study examined existing interRAI data holdings across Ontario, in an attempt to develop three pilot QIs to measure multiple aspects of depression symptom outcomes at a systematic level. The interRAI Child and Youth Mental Health Assessment, a widely used tool implemented in over 100+ Canadian agencies, was used for its homogeneity of reporting and quantity of data. This QI was then risk-adjusted, a process which controls for factors outside the agency's control (e.g., age), and then compared across agencies in Ontario, Canada. Following the introduction of selection criteria, eighteen agencies were included for analysis. Results show support for using interRAI tools in children’s mental health QI development at the agency level. One QI showed extensive variation between agencies, indicative of successful development, while two other QIs showed some variation. Risk adjustment strategy development showed little changes in raw and adjusted QIs. This pilot study will be valuable in informing future QI development initiatives, providing valuable in informing the next steps for quality assessment in Ontario children’s mental healthcare and the development of further interRAI children’s mental health QIs.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".