Prevalence and Correlates of Physical-mental Multimorbidity in Outpatient Children From a Pediatric Hospital in Canada
Bibliographic record
Abstract
Objective The aim of this study was to estimate the six-month prevalence of mental illness in children with chronic physical illness (multimorbidity), examine agreement between parent and child reports of multimorbidity, and identify factors associated with child multimorbidity. Method The sample included 263 children aged 2–16 years with a physician-diagnosed chronic physical illness recruited from the outpatient clinics at a pediatric hospital. Children were categorized by physical illness according to the International Statistical Classification of Diseases and Related Health Problems (ICD)-10. Parent and child-reported six-month mental illness was based on the Mini International Neuropsychiatric Interview for Children and Adolescents (MINI-KID). Results Overall, 101 (38%) of children had a parent-reported mental illness; 29 (25%) children self-reported mental illness. There were no differences in prevalence across ICD-10 classifications. Parent-child agreement on the MINI-KID was low (κ = 0.18), ranging from κ = 0.24 for specific phobia to κ = 0.03 for attention-deficit hyperactivity. From logistic regression modeling (odds ratio [OR] and 95% confidence interval), factors associated with multimorbidity were: child age (OR = 1.16 [1.04, 1.31]), male (OR = 3.76 [1.54, 9.22]), ≥$90,000 household income (OR = 2.57 [1.08, 6.22]), parental symptoms of depression (OR = 1.09 [1.03, 1.14]), and child disability (OR = 1.21 [1.13, 1.30]). Similar results were obtained when modeling number of mental illnesses. Conclusions Findings suggest that six-month multimorbidity is common and similar across different physical illnesses. Level of disability is a robust, potentially modifiable correlate of multimorbidity that can be assessed routinely by health professionals in the pediatric setting to initiate early mental health intervention to reduce the incidence of multimorbidity in children.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| 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".