Enriched supervision to increase quality of early intensive behavioral intervention in autism: a pragmatic randomized controlled pilot study
Bibliographic record
Abstract
Methods to enhance the quality of delivered Early Intensive Behavioral Intervention (EIBI) for children with autism spectrum disorder (ASD) have received surprisingly little attention in previous research. In a pragmatic randomized controlled pilot study, we studied the feasibility and effects of enriched supervision (using detailed video feedback) on EIBI quality compared to regular supervision only, over a period of 4–6 months. EIBI was conducted in 30 children with ASD by preschool staff, where 18 received enriched, and 12 regular supervisions. EIBI quality was evaluated using the York Measure of Quality of Intensive Behavioural Intervention. The enriched supervision was deemed feasible and compared to staff receiving regular supervision. Preschool staff who received enriched supervision improved on overall quality of EIBI delivery, as well as specifically regarding goal-directedness, organization and efficiency of the EIBI. Findings support the significance of adequate education and supervision of EIBI providers for intervention quality assurance.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".