Motor Skills, Social Skills, and Participation of Autistic Children
Bibliographic record
Abstract
Generally, participation levels of autistic children are lower than neurotypical children in social and physical activities. The range of activities that autistic children participate in is also less diverse. Building on previous qualitative work, the current research provides a quantitative perspective to explore the relationship between social skills and motor skills in autistic children, and the association with participation at home, school, and in the community. Caregivers and their 5-to-11-year-old autistic children participated in this research. Motor skills were assessed with performance-based (BOT-2-SF) and caregiver-report measures (MABC-2 checklist). Caregivers also completed questionnaires to quantify social skills (SRS-2) and participation (PEM-CY) patterns. Motor assessments were not correlated, suggesting use of the MABC-2 checklist in conjunction with the BOT-2-SF may not be well-suited for younger participants. Discordant results of proxy (i.e., social/motor skills predicted participation frequency at school, with social skills contributing to the model) and performance-based (i.e., social/motor skills predicted participation frequency at home, and average involvement in the community) reports were attributed to caregiver's focus on fine motor performance in school-based settings, considering the established link between social and fine motor skills in autism. Non-significant findings were explained by heterogeneity in social/motor skills among autistic children. Overall, while continued research is warranted, findings support a person-centered (i.e., individualized) approach to address participation of autistic children.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".