Mental Health Service Use among Children and Youth with Co-Occurring Health and Mental Health Disorders in Ontario
Bibliographic record
Abstract
Children (aged 4-11) and youth (aged 12-17) with chronic health conditions (CHC; e.g., asthma, diabetes) have a higher risk of developing mental health (MH) problems compared to those without CHC. First, we created an algorithm to identify children with CHC using Ontario health administrative data. Using secondary data analysis of administrative and survey data (2014 Ontario Child Health Study), we documented the health (e.g., family physician, specialist) and MH service contacts (e.g., psychologist, psychiatrist) of children with CHC and MH concerns. We explored whether there was an association between CHC visits and MH service use. Family physician visits and CHC visits with a specialist were significantly associated with specialized MH services. Having a perceived need for MH help was the strongest predictor. Having a family physician was strongly related to reduced MH service use. This highlights the need for integrated health and mental healthcare systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".