The Effect of Autism Degree on Children’s Verbal Communication Ability: The Mediating Role of Cognitive Function
Bibliographic record
Abstract
The study examined how the severity of autism affects children’s verbal communication skills, especially focus on the mediating role of cognitive function. Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. It often causes difficulties in not only communication but also social interactions. We invited 200 children aged 3 to 6 years from Beijing to participant in our test. And the aim is to see how the level of autism impacts children’s communication skills by influencing cognitive function. In our research, we used many standardized tools to evaluate the severity of autism, cognitive function, and verbal communication abilities. For example, we used the Childhood Autism Rating Scale (CARS), the Montreal Cognitive Assessment (MoCA), and the Language Impairment Assessment Scale for Preschool Children. There are many ways for us to collect data, such as natural observation and questionnaires. And we analyzed the data by using SPSS and Mplus 8.3 software. The results show that verbal communication skills are different in different groups, and cognitive function plays a key mediating role among them. In conclusion, cognitive function is very important in finding out the truth of how autism severity influences verbal communication. What’s more, this study aims to find out more targeted intervention strategies. In this way, educators can create more personalized teaching methods to help autistic children grow up much more healthily.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".