Parent Training Interventions for Toddlers with Autism Spectrum Disorder
Bibliographic record
Abstract
Background. Now that early identification of toddlers with autism spectrum disorder (ASD) is possible, efforts are being made to develop interventions for children under three years of age. Most studies on early intervention have focused on intensive and individual interventions. However, parent training interventions that help parents interact and communicate with their toddlers with ASD might be a good alternative to promote the development of their child's sociocommunicative skills. Objective. This review aims to systematically examine (1) the use of parent training interventions for children with ASD under three years of age and (2) their effects on children's development, parents' well-being and parent-child interactions. Methods. Systematic searches were conducted to retrieve studies in which at least one parent was trained to implement ASD-specific techniques with their toddlers (0-36 months old) with a diagnosis of or suspected ASD. Results. Fifteen studies, involving 484 children (mean age: 23.26 months), were included in this review. Only two of them met criteria for conclusive evidence. Results show that parents were able to implement newly learned strategies and were generally very satisfied with parent training programs. However, findings pertaining to the children's communication and socioemotional skills, parent-child interactions, and parental well-being were inconclusive.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".