Income and Household Material Hardship in Children With Medical Complexity
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Household economic hardship negatively impacts child health but may not be adequately captured by income. We sought to determine the prevalence of household material hardship (HMH), a measure of household economic hardship, and to examine the relationship between household poverty and material hardship in a population of children with medical complexity. METHODS: We conducted a cross-sectional survey study of parents of children with medical complexity receiving primary care at a tertiary children's hospital. Our main predictor was household income as a percentage of the federal poverty limit (FPL): <50% FPL, 51% to 100% FPL, and >100% FPL. Our outcome was HMH measured as food, housing, and energy insecurity. We performed logistic regression models to calculate adjusted odds ratios of having ≥1 HMH, adjusted for patient and clinical characteristics from surveys and the Pediatric Health Information System. RESULTS: At least 1 material hardship was present in 40.9% of participants and 28.2% of the highest FPL group. Families with incomes <50% FPL and 51% to 100% FPL had ∼75% higher odds of having ≥1 material hardship compared with those with >100% FPL (<50% FPL: odds ratio 1.74 [95% confidence interval: 1.11-2.73], P = .02; 51% to 100% FPL: 1.73 [95% confidence interval: 1.09-2.73], P = .02). CONCLUSIONS: Poverty underestimated household economic hardship. Although households with incomes <100% FPL had higher odds of having ≥1 material hardship, one-quarter of families in the highest FPL group also had ≥1 material hardship.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".