Health Inequity in Children and Youth With Chronic Health Conditions
Bibliographic record
Abstract
BACKGROUND: Over the last decades, there have been great advances in health care delivered to children with chronic conditions, but not all children have benefitted equally from them. OBJECTIVES: To describe health inequities experienced by children with chronic health conditions. METHODS: We performed a literature review of English-language studies identified from the Medline, Centers for Disease Control and Prevention, National Cancer Institute, and Cystic Fibrosis Foundation Web sites that were published between January 1985 and May 2009, included children aged 0 to 18 years, and contained the key words "incidence," "prevalence," "survival," "mortality," or "disparity" in the title or abstract for the following health conditions: acute leukemia, asthma, attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorders, cerebral palsy, cystic fibrosis, diabetes mellitus, Down syndrome, HIV/AIDS, major congenital heart defects, major depressive disorder, sickle cell anemia, spina bifida, and traumatic brain injury. RESULTS: Black children had higher rates of cerebral palsy and HIV/AIDS, were less likely to be diagnosed with ADHD, had more emergency department visits, hospitalizations, and had higher mortality rates associated with asthma; and survived less often with Down syndrome, type 1 diabetes, and traumatic brain injury when compared with white children. Hispanic children had higher rates of spina bifida from Mexico-born mothers, had higher rates of HIV/AIDS and depression, were less likely to be diagnosed with ADHD, had poorer glycemic control with type 1 diabetes, and survived less often with acute leukemia compared with white children. CONCLUSIONS: Serious racial and ethnic health and health care inequities persist for children with chronic health conditions.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".