Relationships Between Material Hardship, Resilience, and Health Care Use
Bibliographic record
Abstract
BACKGROUND: Material hardship has been associated with adverse health care use patterns for children with special health care needs (CSHCN). In this study, we assessed if resilience factors were associated with lower emergency department (ED) visits and unmet health care needs and if they buffered associations between material hardship and health care use for CSHCN and children without special health care needs. METHODS: A cross-sectional study using the 2016 National Survey of Children’s Health, restricted to low-income participants (<200% federal poverty level). Separately, for CSHCN and children without special health care needs, weighted logistic regression was used to measure associations between material hardship, 2 resilience factors (family resilience and neighborhood cohesion), and 2 measures of use. Moderation was assessed using interaction terms. Mediation was assessed using structural equation models. RESULTS: The sample consisted of 11 543 children (weighted: n = 28 465 581); 26% were CSHCN. Material hardship was associated with higher odds of ED visits and unmet health care needs for all children. Resilience factors were associated with lower odds of unmet health care needs for CSHCN (family resilience adjusted odds ratio: 0.58; 95% confidence interval: 0.36–0.94; neighborhood cohesion adjusted odds ratio: 0.53; 95% confidence interval: 0.32–0.88). For CSHCN, lower material hardship mediated associations between resilience factors and unmet health care needs. Neighborhood cohesion moderated the association between material hardship and ED visits (interaction term: P = .02). CONCLUSIONS: Among low-income CSHCN, resilience factors may buffer the effects of material hardship on health care use. Future research should evaluate how resilience factors can be incorporated into programs to support CSHCN.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".