Association between Weight Status and Mental Health Service Utilization in Children and Adolescents.
Bibliographic record
Abstract
BACKGROUND: Previous literature reports inconsistent associations between obesity and mental health. The objective of this study was to determine the association between weight status and mental health service utilization in Ontario children and youth. METHODS: A cross-sectional study of children 0 to 18 years, identified using primary care electronic medical records from the EMRPC database in Ontario, Canada was conducted. Height and weight data were extracted to calculate BMI and linked to administrative data on mental health related outpatient visits, emergency department visits, and hospitalizations. Multivariable logistic regression models were performed. RESULTS: A total of 50,565 children were included. Overall, 2.2% were underweight, 70.4% had a normal weight, 18.3% were overweight, 6.9% had obesity and 2.2% had severe obesity. 28.2% of all children had at least one mental health visit. Multivariable analyses showed children with overweight, obesity, and severe obesity were 1.11 (95% CI 1.05-1.17), 1.18 (95% CI 1.08-1.27) and 1.39 (95% CI 1.22-1.59) times more likely to have an outpatient mental health visit compared to children with normal weight. CONCLUSION: Increased weight status was associated with mental health related outpatient visits and emergency department visits. This study may inform policy makers' planning of mental health resources for children with obesity and severe obesity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".