Development, psychometric evaluation, and factor analysis of an instrument measuring quality of life in autistic preschoolers
Bibliographic record
Abstract
INTRODUCTION: Early interventions for autistic children should target their quality of life (QoL) but require adapted measures. The association of a child's temperament and parental characteristics with the QoL of autistic children remains unknown. METHODS: We constructed an autism module based on a thematic analysis, a Delphi survey with experts, and a pre-test with parents to be completed alongside the proxy version of the PedsQL 4.0. We explored compliance, responsiveness, internal consistency, convergent validity, and factor structure with 157 parents of autistic preschool children. We examined the association between child and parental characteristics with the QoL of autistic children using correlation analysis, principal component analysis, hierarchical ascending classification, and linear regression. Sociodemographic information was collected via multiple choice questions, autism severity via Autism Diagnostic Observation Schedule (ADOS) scores, and parental acceptance and child's temperament via the Acceptance and Action Questionnaire and the Emotionality, Activity, and Sociability. RESULTS: An autism module comprised of 27 items emerged. Psychometric evaluation resulted in a 24-item autism module with good internal consistency and significant convergent validity. ADOS total score was not significantly related to QoL, contrary to children's sleep issues, children's emotionality, and parental acceptance. CONCLUSIONS: The autism module is a reliable QoL proxy measure for autistic preschool children. Results suggest parental interventions targeting children's QoL.
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 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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".