The relationship between childhood asthma and mental health conditions
Bibliographic record
Abstract
Background: Mental health conditions are becoming more common and may have implications on childhood asthma. Objectives: We examined the association between childhood asthma and mental health conditions and if asthma outcomes were worse among children with both asthma and a mental health condition. Methods: In 2013 we completed a cross-sectional survey of urban and rural dwelling children (5-14 years, n=3,509) from Saskatchewan, Canada. Surveys were distributed through randomly selected schools for parental self-completion. Asthma was based on report of a previous doctor9s diagnosis. Presence of a mental health condition was based on a report of doctor’s diagnosis of emotional, psychological, nervous difficulties, or ADD/ADHD. Allergy was based on a report of a respiratory allergy, hayfever, or eczema. Results: Asthma prevalence was 19.1% while the prevalence of mental health conditions was 5.5%. Among those without asthma, 4.8% had a mental health condition while among those with asthma, the prevalence was 8.2% (p<0.001). Allergy affected mental health condition prevalence (allergic with asthma: 10.3%; not allergic with asthma: 3.4%; allergic without asthma: 7.0%; not allergic without asthma: 3.8%; p<0.001). Among those with asthma, if a mental health condition was present, they were more likely to experience nocturnal asthma symptoms (p<0.05) than if a mental health condition was not present. These associations remained after adjustment for confounding. Conclusions: Children with asthma are more likely to experience a mental health condition than children without asthma and this may be related to worse outcomes (nocturnal asthma symptoms). The relationship appears to be driven by allergic disease.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".