Profiles and Predictors of Academic and Social School Functioning among Children with Autism Spectrum Disorder
Bibliographic record
Abstract
Objective: The purpose of the study was to identify profiles and predictors of academic and social functioning in a sample of school-age children with autism spectrum disorder.Method: The study included 178 children (88% boys, 75% Caucasian, ages 10–11) who completed a standardized measure of academic skills and whose teachers completed a related measure. Measures of both academic and social performance were used to construct profiles of school functioning. Measures of language, nonverbal IQ, autism symptom severity, behavior difficulties, and early social-communication skills between ages 3 and 4 were used to examine predictors of profile membership. Latent Profile Analysis was used to identify and describe profiles of children’s academic and social school functioning. Profile membership was then regressed on each of the predictors using a series of multinomial logistic regression models. Finally, a multivariate model that included all significant predictors was built to examine the best fitting constellation of profile predictors.Results: Four profiles – reflecting variation in academic achievement, school engagement, socialization skills, pragmatic language use, and social relationships – captured the diverse school functioning outcomes of the sample. Profile membership was predicted by variation in imitation, responding to joint attention, language ability, nonverbal IQ and behavior difficulties between ages 3 and 4 years. However, in a multivariate model, only language and behavior difficulties emerged as significant predictors.Conclusions: A person-centered approach to targeted early intervention that reduces behavior difficulties and enhances social-communication and language abilities may prove especially important for the promotion of later academic and social functioning at school.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".