High Intensity Physician-Based Service Use for Mental Health Concerns in a General-Population Sample of Children and Youth: Utilisation des services de haute intensité dispensés par des médecins pour les problèmes de santé mentale dans un échantillon d’enfants et de jeunes de la population générale
Bibliographic record
Abstract
OBJECTIVE: To examine factors associated with high intensity physician-based mental health care services in a population-based sample of children and youth in Ontario, Canada. METHODS: = 1,423) included children and youth with at least one physician-based contact for a mental health concern in the 24-month period post-OCHS. Over the same follow-up period, we classified high intensity service use as those in the top 10th and fifth percentiles of physician-based mental health service cost contributors. Costs were assessed using physician billing data, as well as estimated emergency department visit and hospitalization costs. RESULTS: Among those with at least one contact, being older (PR: 1.15, 95% CI: 1.04, 1.25), having more severe symptoms of mental ill-health (PR: 1.04, 95% CI: 1.01, 1.06) and having a history of mental health service use (PR: 3.99, 95% CI: 1.37, 11.61), were positively associated with high-intensity service use, while living in a rural setting (PR: 0.35, 95% CI: 0.15, 0.30) was negatively associated. Findings were largely consistent between the top 10th and fifth percentiles. Notably, among youth ages 14-17 years, self-reported prior suicide attempt was positively associated with high-intensity (top fifth percentile) service use (PR: 6.09, 95% CI: 1.41, 26.26). CONCLUSIONS: Our findings suggest older age, non-rural residency, mental health symptom severity and suicidal behaviour are important factors associated with high-intensity physician-based mental health service use. Our findings will inform efforts to better identify children and youth who may benefit from early and personalized interventions. PLAIN LANGUAGE SUMMARY TITLE: Understanding Children and Youth with the Greatest Mental Health Related Service Needs.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".