Reasons for Unmet Need for Child and Family Health Services among Children with Special Health Care Needs with and without Medical Homes
Bibliographic record
Abstract
Medical homes, an important component of U.S. health reform, were first developed to help families of children with special health care needs (CSHCN) find and coordinate services, and reduce their children's unmet need for health services. We hypothesize that CSHCN lacking medical homes are more likely than those with medical homes to report health system delivery or coverage problems as the specific reasons for unmet need. Data are from the 2005-2006 National Survey of Children with Special Health Care Needs (NS-CSHCN), a national, population-based survey of 40,723 CSHCN. We studied whether lacking a medical home was associated with 9 specific reasons for unmet need for 11 types of medical services, controlling for health insurance, child's health, and sociodemographic characteristics. Weighted to the national population, 17% of CSHCN reported at least one unmet health service need in the previous year. CSHCN without medical homes were 2 to 3 times as likely to report unmet need for child or family health services, and more likely to report no referral (OR= 3.3), dissatisfaction with provider (OR=2.5), service not available in area (OR= 2.1), can't find provider who accepts insurance (OR=1.8), and health plan problems (OR=1.4) as reasons for unmet need (all p<0.05). CSHCN without medical homes were more likely than those with medical homes to report health system delivery or coverage reasons for unmet child health service needs. Attributable risk estimates suggest that if the 50% of CSHCN who lacked medical homes had one, overall unmet need for child health services could be reduced by as much as 35% and unmet need for family health services by 40%.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 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".