The Development of Quality Indicators to Assess Family Wellbeing Outcomes Following Engagement with Children’s Mental Health Services in Ontario, Canada
Bibliographic record
Abstract
(1) Background: Caregivers and families of children involved with mental health services face unique challenges. In Ontario, there is a dearth of information on outcomes for families following a child's involvement with mental health services. Metrics known as Quality Indicators (QIs) offer a way to better understand these outcomes. Importantly, QIs can be risk adjusted to account for the influence of client complexity to allow for fair inter-agency comparisons. This study developed a set of risk-adjusted caregiver/family outcome QIs for children's mental healthcare agencies. (2) Methods: Archival data from widely implemented interRAI child and youth assessment instruments was used. Previous methodology for QI calculation and risk adjustment was adapted and tested. (3) Results: Utilizing the interRAI suite of child and youth assessment instruments, a set of six QIs focusing on improvement or decline in parenting strengths, caregiver distress, and family functioning were developed. (4) Conclusions: The QIs established were sufficiently independent to represent different aspects of family wellbeing while the risk adjustment strategy developed was useful in removing client complexity from QI calculation. Implications for future directions, including the use of QIs at a systems level to more accurately direct resources and set performance benchmarks, are discussed.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".