Child functional characteristics explain child and family outcomes better than diagnosis: Population-based study of children with autism or other neurodevelopmental disorders/disabilities.
Bibliographic record
Abstract
BACKGROUND: Allocation of resources for services and supports for children with neurodevelopmental disorders/disabilities (NDD/D) is often based on the presence of specific health conditions. This study investigated the relative roles of a child's diagnosed health condition and neurodevelopmental and related functional characteristics in explaining child and family health and well-being. DATA AND METHODS: The data on children with NDD/D (ages 5 to 14; weighted n = 120,700) are from the 2006 Participation and Activity Limitation Survey (PALS), a population-based Canadian survey of parents of children with functional limitations/disabilities. Direct and indirect effects of child diagnosis status-autism spectrum disorder (ASD)/not ASD-and functional characteristics (particularly, ASD-related impairments in speech, cognition, and emotion and behaviour) on child participation and family health and well-being were investigated in a series of structural equation models, while controlling for covariates. RESULTS: All models adequately fitted the data. Child ASD diagnosis was significantly associated with child participation and family health and well-being. When ASD-related child functional characteristics were added to the model, all direct effects from child diagnosis on child and family outcomes disappeared; the effect of child diagnosis on child and family outcomes was fully mediated via ASD-related child functional characteristics. INTERPRETATION: Children's neurodevelopmental functional characteristics are integral to understanding the child and family health-related impact of neurodevelopmental disorders such as ASD. These findings have implications for the relative weighting given to functional versus diagnosis-specific factors in considering needs for services and supports.
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.
How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".