Teaching parents to promote language use of children with autism spectrum disorders within family routines using enhanced milieu teaching
Bibliographic record
Abstract
Children with autism spectrum disorder (ASD) often do not acquire language naturally within ecological systems (i.e., parent-child interaction in daily routines); therefore some of these children have significant delays in social communication skills. Language interventions such as discrete-trial teaching procedures, the verbal behavior approach, and naturalistic language teaching approaches have been developed to improve language use among children with ASD. However, few research studies have examined the generalization and maintenance effects of language intervention implemented by parents on child’s communication skills across natural family routines. The purpose of this study is to evaluate the effectiveness of a language intervention model that synthesizes three theoretical frameworks, enhanced milieu language teaching (EMT), general case programming principles, and the activity setting (i.e., daily or weekly routine) as a unit of analysis and intervention for promoting generalized language use by young children with ASD. The study employed an empirical case study design with one parent-child dyad. Parent training was presented in a two-day workshop. Results showed improvements in parent use of EMT and in child use of language in indirectly trained and non-trained (i.e., generalization) family routines in the home. These improvements maintained at one and two months post-intervention. The results are discussed with reference to previous research, contributes, future directions, and implications for practitioners and researchers who are involved in language promotion interventions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".