Profile and predictors of service needs for families of children with autism spectrum disorders
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Increasing demand for autism services is straining service systems. Tailoring services to best meet families' needs could improve their quality of life and decrease burden on the system. We explored overall, best, and worst met service needs, and predictors of those needs, for families of children with autism spectrum disorders. METHODS: Parents of 143 children with autism spectrum disorders (2-18 years) completed a survey including demographic and descriptive information, the Family Needs Survey-Revised, and an open-ended question about service needs. Descriptive statistics characterize the sample and determine the degree to which items were identified and met as needs. Predictors of total and unmet needs were modeled with regression or generalized linear model. Qualitative responses were thematically analyzed. RESULTS: The most frequently identified overall and unmet service needs were information on services, family support, and respite care. The funding and quality of professional support available were viewed positively. Decreased child's age and income and being an older mother predicted more total needs. Having an older child or mother, lower income, and disruptive behaviors predicted more total unmet needs, yet only disruptive behaviors predicted proportional unmet need. Child's language or intellectual abilities did not predict needs. CONCLUSION: Findings can help professionals, funders, and policy-makers tailor services to best meet families' needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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 it