Predictive Impact of Resilience on Depressive Symptoms in Adolescents with High Functioning Autism Spectrum Disorders
Bibliographic record
Abstract
Adolescents with high functioning Autism Spectrum Disorders (ASDs) are highly vulnerable to depressive symptoms (DS) and a range of mental health problems compared to their typically developing peers. It is not known whether resilience can influence DS in adolescents with high functioning ASD. This study sought to find out the link between resilience and DS in a sample of adolescents with high functioning ASD in Nigeria. The study is a quantitative correlation study of in-school adolescents with high functioning ASD. 68 adolescents with high functioning ASD from 20 inclusive education schools participated in the study. Data were collected using self report versions of Child and Youth resilience Measure (CYRM-SR) and Children’s Depression Inventory second edition (CDI-2: SR). Findings showed that total resilience score is a strong negative predictor of DS in adolescents with high functioning ASD (B=-.93; β=-.77; t=-4.20; p=.000). Specifically, individual capacities subscale (B=-2.20; β=-.77;t=-8.39;p=.000), Primary caregivers resources subscale (B=-1.98; β=-.69; t=-7.49; p=.000); and Contextual factors subscale (B=-2.02; β=-.62; t=-8.38; p=.000) predicted overall depressive symptoms (Total DS score) negatively and significantly. It was concluded that DS among adolescents with high functioning ASD can be reduced through developing resilience skills among them. Parents, special Educators and all stakeholders should intensify efforts in building resilience in adolescents with high functioning ASD.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".