Mental health of siblings of children with physical illness or physical–mental comorbidity
Bibliographic record
Abstract
OBJECTIVES: This study examined the mental health of siblings of children with physical illness (PI), with or without co-occurring mental illness. METHODS: The sample included children aged 2 to 16 years with a chronic PI and their aged-matched healthy siblings (n = 169 dyads). Physical-mental comorbidity (PM) was present if children screened positive for ≥1 mental illness on the Mini International Neuropsychiatric Interview for Children and Adolescents. Parents completed the Strengths and Difficulties Questionnaire (SDQ) to measure child and sibling mental health. RESULTS: Within child-sibling dyads, siblings of children with PI had significantly worse mental health related to conduct problems (d = 0.31), peer problems (d = 0.18) and total difficulties (d = 0.20). Siblings of children with PM had significantly better mental health related to emotional problems (d = 0.42), hyperactivity/inattention (d = 0.23) and total difficulties (d = 0.32). Siblings of children with PI had similar mental health compared with child population norms used in the development of the SDQ. In contrast, siblings of children with PM had significantly worse mental health across all SDQ domains, with the exception of prosocial behaviour. After adjusting for parent psychopathology and family functioning, no statistically significant differences between siblings of children with PM versus siblings of children with PI were found. CONCLUSIONS: Differences in mental health exist between children with PI or PM versus their healthy siblings. However, differences between siblings of children with PI versus siblings of children with PM can be explained by parental and family factors (e.g. marital status, education and income). Findings reinforce family-centred care approaches to address the needs of children with PI or PM and their families.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".