Children’s mental health need in Ontario: measurement, variations in unmet need and the alignment between children’s mental health service expenditures and need
Bibliographic record
Abstract
This thesis draws on the 2014 Ontario Child Health Study (2014 OCHS) to address four contemporary and policy-relevant issues associated with measuring child and adolescent mental health need and children’s mental health service use in the general population. The first and second papers focus on the development and evaluation of instruments to measure child mental disorder. The first paper develops a simple, brief symptom checklist used to measure child mental disorder conceptualized as a dimensional phenomenon, a core concept in the 2014 OCHS. The second focuses on a briefer version of this checklist to measure child mental disorder dimensionally in general and clinical populations for the purposes of assessing and monitoring children’s mental health need. The third and fourth papers use these measures as the basis for assessing children’s mental health need in evaluations of policy-relevant health service questions. The third paper focuses on a substantive question about area-level variation in children’s unmet need for mental health services using 2014 OCHS data linked to government administrative data and 2016 Census data. The fourth paper estimates the extent to which child mental health service expenditures in 2014-15 were allocated according to children’s mental health need. Together, these papers respond to the need for simple, brief, self-report measures of child and adolescent mental disorders and show how these types of measures, in combination with administrative government data sources can advance our knowledge about policy and funding decisions in children’s mental health services research in Ontario.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".