Development of the Resource Use Questionnaire (RUQ–P) for Families With Preschool Children With Neurodevelopmental Disorders: Validation in Children With Autism Spectrum Disorder
Bibliographic record
Abstract
Cost-effectiveness and burden-of-illness research of pediatric neurodevelopmental disorders (NDDs) requires accurate and comprehensive captures of health and non-health-sector costs and family out-of-pocket costs. The objective was to develop and validate the Resource Use Questionnaire—Preschool (RUQ–P) as a tool to capture intersectoral service use and family out-of-pocket costs in preschool children with an NDD from multiple-payer perspectives. The major categories of the RUQ–P include primary intervention, such as programs based on applied behavior analysis; childcare or school programs; other interventions, such as speech−language therapy or occupational therapy; other resources, such as medications, respite support, complementary and alternative medicine, and other services and treatments; productivity losses; and government subsidies and charitable gifts. The RUQ–P targeted preschool children ages 2–6 years with an NDD with completion by interview with the primary caregiver. It underwent pilot testing and was validated in a cohort of children in the Preschool Autism Treatment Impact study comparing early preschool behavioral interventions in Nova Scotia and New Brunswick, Canada. The RUQ–P collected data on resource use; service intensity; and out-of-pocket costs, such as hourly rates, costs of materials, and travel costs. A cost analysis was undertaken to determine whether the RUQ–P was responsive to differences between treatment groups and changes over time. Modifications were subsequently made to reduce respondent burden and missing data (i.e., more closed-ended and structured questions and removal of travel costs). Versions of the RUQ are being developed for 3 additional age groups, and the tool is being further validated in observational and experimental studies.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".