An Exploration of a Community-Based LEGO® Social-Skills Program for Youth with Autism Spectrum Disorder
Bibliographic record
Abstract
Clinical interventions have been effective at increasing social skills of youth with autism spectrum disorder (ASD). However, generalization of those skills to non-clinical environments is often low. To reduce this generalization gap, community-based programs have been designed to help youth develop social skills in naturalistic settings. This paper describes a community-based social-skills intervention for youths with ASD aged 7–12, which was designed to build on the findings of a previous study. In this program, youths with ASD co-operated with siblings and peers in structured and unstructured play over the course of four weeks. The researchers conducted extensive observations of the play by the youths and conducted interviews with the youths with ASD, their parents, and program staff. Both in our observations and in the perceptions of the parents, the youths with ASD increased their play and socialization. Using Vygotsky’s (1978) socio-cultural theory as a guiding framework, we describe the components of this intervention that contributed to the changes observed in the youths’ play and social interactions. We developed a model that includes components of instruction, interest, play- based learning, and the structure of program, and which provides an explanation of intervention effectiveness to be explored in future research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".