Chronic illnesses in Canadian children: what is the effect of illness on academic achievement, and anxiety and emotional disorders?
Bibliographic record
Abstract
BACKGROUND: Survival rates of children with a chronic illness is at an all-time high. Up to 98% of children suffering from a chronic illness, which may have been considered fatal in the past, now reach early adulthood. It is estimated that as many as 30% of school-aged children are affected by a chronic illness. For this population of children, the prevalence of educational and psychological problems is nearly double in comparison with the general population. METHODS: This study investigated the educational and psychological effects of childhood chronic illness among 1512 Canadian children (ages 10-15 years). This was a retrospective analysis using data from the National Longitudinal Survey of Children and Youth, taking a cross-sectional look at the relationships between childhood chronic illnesses, performance on a Mathematics Computation Exercise (MCE) and ratings on an Anxiety and Emotional Disorder (AED) scale. RESULTS: When AED ratings and educational handicaps were controlled for, children identified with chronic illnesses still had weaker performance on the MCE. Chronic illness did not appear to have a relationship with children's AED ratings. The regression analysis indicated that community type and illness were the strongest predictors of MCE scores. CONCLUSIONS: The core research implications of this study concern measurement issues that need to be addressed in future large-scale studies. Clinical implications of this research concern the need for co-ordinated services between the home, hospital and school settings so that services and programmes focus on the ecology of the child who is ill.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".