Social-Skill Interventions for Culturally and Linguistically Diverse Students with Disabilities: A Comprehensive Review
Bibliographic record
Abstract
Teachers and researchers have considered social-skill interventions to be an essential component in the development and progress of students with disabilities. However, there is still relatively limited research on these interventions for individuals from culturally and linguistically diverse (CLD) backgrounds. This literature review was conducted to examine the effectiveness of social-skill interventions for CLD students with disabilities in school settings. Electronic database searches and a manual search were completed to identify studies published between 2000 and 2017 (February). Seven studies (n = 18 participants) were identified for inclusion in this review, and five types of social interventions were identified. Most participants were male, aged between 8 and 13 years old, were considered at risk for having developmental delay or had developmental delay, and were identified as African Americans. The majority of studies we reviewed utilized single-subject research designs and focused on social interactions as the goal for their individual interventions. Peer-mediated interventions and social story intervention were the most frequently used interventions. Findings suggest that, when exposed to the social-skill interventions, CLD children with disabilities improved their social behaviours and skills. Some children with disabilities maintained and generalized these behaviours across settings or playmates.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".