Children’s Mental Health Need and Expenditures in Ontario: Findings from the 2014 Ontario Child Health Study
Bibliographic record
Abstract
OBJECTIVE: To estimate the alignment between the Ontario Ministry of Children and Youth Services (MCYS) expenditures for children's mental health services and population need, and to quantify the value of adjusting for need in addition to population size in formula-based expenditure allocations. Two need definitions are used: "assessed need," as the presence of a mental disorder, and "perceived need," as the subjective perception of a mental health problem. METHODS: Children's mental health need and service contact estimates (from the 2014 Ontario Child Health Study), expenditure data (from government administrative data), and population counts (from the 2011 Canadian Census) were combined to generate formula-based expenditure allocations based on 1) population size and 2) need (population size adjusted for levels of need). Allocations were compared at the service area and region level and for the 2 need definitions (assessed and perceived). RESULTS: Comparisons were made for 13 of 33 MCYS service areas and all 5 regions. The percentage of MCYS expenditure reallocation needed to achieve an allocation based on assessed need was 25.5% at the service area level and 25.6% at the region level. Based on perceived need, these amounts were 19.4% and 27.2%, respectively. The value of needs-adjustment ranged from 8.0% to 22.7% of total expenditures, depending on the definition of need. CONCLUSION: Making needs adjustments to population counts using population estimates of children's mental health need (assessed or perceived) provides additional value for informing and evaluating allocation decisions. This study provides much-needed and current information about the match between expenditures and children's mental health need.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 teacher head, 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".