Correlates of single morbidities and multimorbidity in children: A cross-sectional study
Bibliographic record
Abstract
This study investigated and compared correlates of multimorbidity with other single morbidity statuses (physical illness only, mental disorder only, neurodevelopmental disorder only) among children in Canada. The epidemiological sample included 33,715 children aged 5-17 years from the Canadian Health Survey of Children and Youth. Classification of children by morbidity status was based on reports from the person most knowledgeable (PMK). Multinomial logistic regression quantified associations between demographic and psychosocial characteristics and morbidity status using odds ratios (ORs) and 95 % confidence intervals (CIs). Female (OR=0.5 [0.5-0.6]) and immigrant children (OR=0.6 [0.5-0.8]) were less likely to report multimorbidity, as well as singular morbidity statuses. Older children (OR=2.3 [2.1-2.6]) were more likely to report multimorbidity. Elevated parent stress (OR:2.1 [1.7-2.5]), worse parent mental health (OR=3.0 [2.4-3.7]), and communities perceived as less safe (OR:1.5 [1.2-2.0]) were associated with higher odds of multimorbidity. Differences in magnitudes of association across morbidity statuses for child age and sex, as well as PMK mental health, and stress levels represent opportunities to identify at-risk children to aid in the prevention of multimorbidity. Strong associations between parent stress and mental health and child morbidity highlight the need to adopt integrated health services that use a family-centred model of care.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".