Internalizing–Externalizing Comorbidity and Impaired Functioning in Children
Bibliographic record
Abstract
BACKGROUND: The comorbidity of mental illnesses is common in child and adolescent psychiatry. Children with internalizing-externalizing comorbidity often experience worse health outcomes compared to children with a single diagnosis. Greater knowledge of functioning among children with internalizing-externalizing comorbidity can help improve mental health care. OBJECTIVE: The objective of this exploratory study was to examine whether internalizing-externalizing comorbidity was associated with impaired functioning in children currently receiving mental health services. METHODS: The data came from a cross-sectional clinical sample of 100 children aged 4-17 with mental illness and their parents recruited from an academic pediatric hospital. The current mental illnesses in children were measured using the Mini International Neuropsychiatric Interview for Children and Adolescents (MINI-KID), and the level of functioning was measured using the World Health Organization Disability Assessment Schedule (WHODAS) 2.0. Linear regression was used to estimate the association between internalizing-externalizing comorbidity and level of functioning, adjusting for demographic, psychosocial, and geographic covariates. RESULTS: = 0.049) were associated with worse functioning in children. CONCLUSIONS: Health professionals should be mindful that children with internalizing-externalizing comorbidity may experience worsening functioning that is disruptive to daily activities and should use this information when making decisions about care. Given the exploratory nature of this study, additional research with larger and more diverse samples of children is warranted.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".