Parent-reported health care expenditures associated with autism spectrum disorders in Heilongjiang province, China
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to determine the health expenses incurred by families with children with autism spectrum disorder (ASD) and those expenses' relation to total household income and expenditures. METHODS: In this cross-sectional study, health care expenditure data were collected through face-to-face interviews. Expenses included annual costs for clinic visits, medication, behavioral therapy, transportation, and accommodations. Health care costs as a percentage of total household income and expenditures were also determined. The participants included 290 families with ASD children who were treated at the Children Development and Behavior Research Center, Harbin Medical University, China. RESULTS: Families with ASD children from urban and rural areas had higher per-capita household expenditures by 60.8% and 74.7%, respectively, compared with provincial statistics for 2007. Behavioral therapy accounted for the largest proportion of health expenses (54.3%) for ASD children. In 19.9% of urban and 38.2% of rural families, health care costs exceeded the total annual household income. Most families (89.3% of urban families; 88.1% of rural families) in that province reported higher health care expenditures than the provincial household average. CONCLUSION: For families with ASD children, the economic burden of health care is substantially higher than the provincial average.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".