Impact of Adverse Childhood Experiences on Resilience and School Success in Individuals With Autism Spectrum Disorder and Attention-Deficit Hyperactivity Disorder
Bibliographic record
Abstract
Adolescents with emotional and behavioral disorders face known academic challenges and poor life outcomes. It was imperative to explore and find if the new diagnostic criterion for diagnosing autism profoundly affects educational outcomes and resilience in individuals diagnosed with co-occurring autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). The literature is robust on the impact of adverse childhood experiences (ACEs) on educational outcomes and resilience in adolescents with no history of disability. Still, there remains a dearth of literature explaining, with no ambiguity, the complex relationships between ACEs and resilience, school engagement, and success in individuals with co-occurring ASD and ADHD. This study reviews the existing scholarships on the topic. The significance of this review is that it informs healthcare providers, rehabilitation counselors, and educators about the need for early identification of individuals with ASD and ADHD with a background in ACEs. This will enable interventions early enough to ensure they are more resilient and can obtain improved success in school-related and outside-school activities and eventually improved quality of life.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".