Empathy and Adaptive Behavior as Predictors of Neurodevelopmental Functioning in Adolescents with Autism Spectrum Disorder
Bibliographic record
Abstract
This study aims to investigate the predictive relationship between empathy, adaptive behavior, and neurodevelopmental functioning in adolescents with Autism Spectrum Disorder (ASD). Understanding how these factors interplay can inform targeted interventions to enhance the quality of life and developmental outcomes for this population. The study employed a cross-sectional design with a sample of 330 adolescents diagnosed with ASD, aged 12 to 18 years. Participants were assessed using the Vineland Adaptive Behavior Scales, Third Edition (Vineland-3) for neurodevelopmental functioning, the Interpersonal Reactivity Index (IRI) for empathy, and the Adaptive Behavior Assessment System, Third Edition (ABAS-3) for adaptive behavior. Data analysis included descriptive statistics, Pearson correlation, and linear regression using SPSS-27. Descriptive statistics revealed mean scores of 85.45 (SD = 15.23) for neurodevelopmental functioning, 52.30 (SD = 8.57) for empathy, and 70.12 (SD = 10.34) for adaptive behavior. Pearson correlation analysis showed significant positive correlations between neurodevelopmental functioning and both empathy (r = .56, p < .001) and adaptive behavior (r = .67, p < .001). The regression analysis indicated that empathy (β = .38, p < .001) and adaptive behavior (β = .52, p < .001) significantly predict neurodevelopmental functioning, explaining 55% of the variance (R² = .55, p < .001). The study findings highlight the significant roles of empathy and adaptive behavior in predicting neurodevelopmental functioning in adolescents with ASD. Interventions focusing on enhancing these areas could positively impact the overall development and daily functioning of individuals with ASD. Future research should utilize longitudinal designs to further explore these relationships and examine the effectiveness of targeted interventions.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".