Health outcomes in low-income children with current asthma in Canada
Bibliographic record
Abstract
Data collected from the Canadian National Longitudinal Survey of Children and Youth (NLSCY) in 1994/95 and 1996/97 were used to measure longitudinal health outcomes among children with asthma. Over 10 000 children aged 1 to 11 years with complete data on asthma status in both years were included. Outcomes included hospitalizations and health services use (HSU). Current asthma was defined as children diagnosed with asthma by a physician and who took prescribed inhalants regularly, had wheezing or an attack in the previous year, or had their activities limited by asthma. Children having asthma significantly increased their odds of hospitalization (OR = 2.52; 95% CI: 1.71, 3.70) and health services use (OR = 3.80; 95% CI: 2.69, 5.37). Low-income adequacy (LIA) in 1994/ 95 significantly predicts hospitalization and HSU in 1996/97 (OR = 2.68; 95% CI: 1.29, 5.59 and OR = 0.67; 95% CI: 0.45, 0.99, respectively). Our results confirmed that both having current asthma and living in low-income families had a significant impact on the health status of children in Canada. Programs seeking to decrease the economic burden of pediatric hospitalizations need to focus on asthma and low-income populations.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".