Developmental health of Canadian kindergarten children with teacher-reported asthma between 2010 and 2015: A population-level cross-sectional study
Bibliographic record
Abstract
Asthma can impact children’s quality of life. It is unclear how asthma is associated with the developmental health (i.e. a broad range of skills and abilities associated with growth and development) of young children at school entry. The goals of this cross-sectional, population-level study were to: (1) investigate the association between teacher-reported asthma and children’s concurrent indicators of developmental health (developmental vulnerability); and (2) explore whether school absences and functional impairments modified this association. Participants were a Canadian population-based sample of 564 582 kindergarten children (Mage = 5.71 years, SD = 0.32, 51.3 % male) with data on the Early Development Instrument (EDI) collected between 2010 and 2015. Adjusted binary logistic regressions were conducted to address the objectives. From the sample, 958 (0.2 %) children were identified as having a diagnosis of asthma. These children were absent on average 9.4 days and 53.5 % had functional impairments (vs. 6.7 days absent and 15.9 % with functional impairments in children without asthma). After controlling for demographic characteristics, children with asthma had between 1.51 and 2.42 higher odds of being developmentally vulnerable. Only the presence of functional impairments modified this relationship and only for physical health and well-being. In this large, population-based sample of Canadian kindergarten children, few teachers reported knowledge of their students’ asthma diagnosis. Among teacher-reported cases, asthma was a risk factor for developmental vulnerability in the domain of physical health and well-being only. Functional impairments may therefore be more detrimental for child development at school entry than asthma alone.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".