How Much Time Do Families Spend on the Health Care of Children with Diabetes?
Bibliographic record
Abstract
Introduction: Family time caring for children with diabetes is an overlooked component of the overall burden of the condition. We document and analyze risk factors for time family members spend providing health care at home and arranging/coordinating health care for children with diabetes. Methods: Data for 755 diabetic children and 16,161 non-diabetic children whose chronic conditions required only prescription (Rx) medication were from the 2009-2010 United States National Survey of Children with Special Health Care Needs (NS-CSHCN). We used generalized ordered logistic regressions to estimate adjusted odds ratios (AORs) of time burden by diabetes, insulin use, and stability of the child's health care needs, controlling for health and socioeconomic status. Results: Nearly one-quarter of diabetic children had family members who spent 11+ h/week providing health care at home, and 8% spent 11+ h/week arranging/coordinating care, compared with 3.3% and 1.9%, respectively, of non-diabetic Rx-only children. Time providing care at home for insulin-using children was concentrated in the higher time categories: AORs for insulin-using diabetic compared to non-diabetic Rx-only children were 4.4 for 1+ h/week compared with <1 h/week, 9.7 for 6+ vs. <6 h, and 12.4 for 11+ vs. <11 h (all P < 0.05); the pattern was less pronounced for non-insulin-using children. AORs for arranging/coordinating care did not vary by time contrast: AOR = 4.2 for insulin-using, 3.0 for non-insulin-using children. Conclusion: Health care providers, school personnel, and policymakers need to work with family members to improve care coordination and identify other ways to reduce family time burdens caring for children with diabetes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".