LONG-TERM HEALTHCARE UTILISATION AMONG CHILDREN WITH MAJOR CHRONIC CONDITIONS: A LARGE POPULATION-BASED COHORT STUDY, ONTARIO 1993–2004
Bibliographic record
Abstract
By improving survival among children diagnosed with major diseases, evaluating the impact of these conditions on children, their healthcare utilisation and the healthcare system is becoming more and more important. These children live with disabling consequences of their diseases, treatment for their conditions, or disabilities, which put them in a specific position with respect to their healthcare needs. Children from poor families represent a greater share of ill health. Improving and providing greater equity in health coverage could greatly reduce health disparities. Our main objectives are to evaluate the socioeconomic determinants and pattern of health services utilisation; specifically, where they receive their access to specialists, specialised referral centres and continuity of care among children diagnosed with major chronic conditions (MCC). Among all children born in Ontario during 1993–2004, children diagnosed with MCC and with major birth defects (MBD) were identified. Healthy children and those born with very low birth weight (LBW) were used as controls. They were followed till the end of 2007 and data on hospitalisation, physician visits and emergency room visits were collected. Among 1 604 000 children born in Ontario, 7200 diagnosed with MCC, 112 000 with MBD, 8400 with LBW and 1 409 000 healthy children were identified. The incidence rate of MCC among children from low income areas was 15% higher than the rate in wealthy areas (4.7/1000 vs 4.1/1000). Similar results were found for MBD (relative risk (RR) 1.21) and LBW (RR 1.32). The early results of this ongoing cohort study, including trend over time for hospitalisations, physicians and emergency room visits, will be presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| 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".