Exploring the Body Language of Children with Autism Spectrum Disorder (ASD) during Physical Activity
Bibliographic record
Abstract
Introduction: Children with autism spectrum disorder (ASD) participate in less physical activity (PA) than their peers. Research has shown that one reason is that some instructors lack awareness about ASD and how best to support these children. Differences with body language expression are a feature of ASD that limit social interactions and may compromise participation. This dissertation used mixed methods to explore how body language of children with ASD is demonstrated and perceived in PA settings and reports the first-stage validation and associated refinement of the Body Language Coding Scale (BLCS) to help instructors with body language identification and interpretation.Methods: Twelve parents of children with ASD (7-12 years) were interviewed to explore thoughts about their child’s body language and their impressions of its interpretation by PA instructors. Next, the BLCS was used to quantify body language during a motor assessment performed by 31 children with ASD (6-12 years). BLCS reliability and its association with motor proficiency and skill enjoyment were evaluated. A content validity evaluation was undertaken with three parents of children with ASD (8-12 years) to review cue valence. The BLCS scoring was revised and reliability was re-evaluated. Results: Misinterpretation of non-basic emotions expressed through body language was identified by parents as a knowledge gap. The BLCS showed excellent intra-rater and moderate inter-rater reliability. No significant associations were shown between body language, motor performance and enjoyment. Parents’ evaluation of cue meaning demonstrated concurrence with BLCS cue valence. Further refinement of the BLCS (including renaming of positive/negative categories to engaged/not engaged) resulted in the BLCS-v2-revised, which demonstrated excellent intra-rater and good inter-rater total score reliability, and excellent intra- and inter-rater category reliability. Discussion: The BLCS-v2-revised is a reliable and valid measure of body language of children with ASD. Further research is needed on the feasibility of using the BLCS-v2-revised in the community context. Conclusion: The BLCS is ready for introduction to PA instructors as a tool to help them communicate better with children with ASD. Bringing awareness of body language differences in children with ASD may help instructors create a more inclusive environment to support participation and enjoyment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".