The Impact of Emotional Maturity and Social Problem-Solving Abilities on Neurobehavioral Outcomes in Autistic Adolescents
Bibliographic record
Abstract
This study aims to investigate the predictive value of emotional maturity and problem-solving skills on neurobehavioral functioning in adolescents with autism. A cross-sectional design was employed, involving 282 adolescents aged 12-18 diagnosed with autism spectrum disorder (ASD). Participants were assessed using the Behavior Assessment System for Children, Second Edition (BASC-2) for neurobehavioral functioning, the Emotional Maturity Scale (EMS), and the Social Problem-Solving Inventory-Revised (SPSI-R). Pearson correlation analysis and linear regression were conducted using SPSS version 27 to examine the relationships and predictive power of emotional maturity and problem-solving skills on neurobehavioral functioning. Descriptive statistics revealed mean scores of 78.54 (SD = 10.37) for neurobehavioral functioning, 84.29 (SD = 12.51) for emotional maturity, and 81.75 (SD = 11.68) for problem-solving skills. Pearson correlation showed significant positive correlations between neurobehavioral functioning and emotional maturity (r = 0.45, p = 0.001), and problem-solving skills (r = 0.38, p = 0.005). Regression analysis indicated that emotional maturity (B = 0.32, p = 0.001) and problem-solving skills (B = 0.29, p = 0.005) significantly predicted neurobehavioral functioning, explaining 27% of the variance (R² = 0.27). The study demonstrates that higher levels of emotional maturity and problem-solving skills are associated with better neurobehavioral functioning in adolescents with autism. These findings underscore the importance of incorporating emotional and cognitive skill development in interventions to enhance neurobehavioral outcomes for this population. Future research should adopt longitudinal designs to explore these relationships over time and include diverse populations for broader applicability.
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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 | 0.003 |
| 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 | 0.001 | 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 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".