Bibliographic record
Abstract
BACKGROUND: The impact of asthma on school performance particularly compared with that of other chronic conditions, is relatively unexplored, and the results of analyses that have been conducted are inconclusive. This article examines associations between asthma and school functioning. DATA AND METHODS: The data are from the 1998/1999 National Longitudinal Survey of Children and Youth. The study pertains to a sample of 8,914 children aged 7 to 15. Descriptive and regression analyses were used to examine associations between asthma severity and scores on standardized math and reading tests, and maternal ratings of school performance. School absence and the use of educational services were considered as potential mediators. Comparisons were made with children who had other chronic conditions or no chronic conditions. RESULTS: Compared with children who did not have a chronic condition, children with asthma scored lower on standardized math and reading tests and had less favourable mother-reported school performance. Those with the most severe asthma had the poorest outcomes. These associations persisted when adjusting for child and family factors. The poorer scholastic outcomes were not mediated by school absence. However, the use of educational services appeared to mediate low math scores for children with severe asthma. INTERPRETATION: The relationship between asthma and children's school functioning may be of interest to physicians and educators. Educational support and remedial services may be beneficial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".