Exploratory examination of the association between physical-mental multimorbidity and physical activity in children
Bibliographic record
Abstract
Children with physical illnesses often experience co-occurring mental illness (known as multimorbidity; MM) and it is currently unknown if MM is associated with physical activity (PA) and if the association differs between internalizing and externalizing disorders. Therefore, the aim of this study was to examine the association between MM and PA. Baseline data from the Multimorbidity in Children and Youth Across the Life Course (MY LIFE) cohort was used. MY LIFE is an ongoing prospective study that follows children ages 2 to 16 years with a chronic physical illness and measures PA using accelerometry and mental illness using the Mini International Neuropsychiatric Interview for Children and Adolescents. 140 children (53.2%) provided valid accelerometer data. Children with internalizing disorders recorded less light (B = -5.87), moderate (B = -1.82), and vigorous PA (B = -1.93) and fewer days meeting PA guidelines [Exp(B) = 0.73] and those with externalizing disorders recorded more light (B = 4.85), moderate (B = 1.78), and vigorous PA (B = 2.41) and more days meeting PA guidelines [Exp(B) = 1.06]. However, only the association between internalizing disorder and days meeting PA guidelines was statistically significant. This study provides preliminary evidence that children with MM may accumulate less PA depending on the type of mental illness they experience.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".