Feasibility of Training Early Childhood Educators in a Community Child Care Setting Using a Caregiver-mediated Intervention for Toddlers with Autism Spectrum Disorder
Bibliographic record
Abstract
Parent-mediated intervention programs have demonstrated benefits for toddlers with autism spectrum disorder (ASD). Interest is emerging in other community-level models, such as those that can be integrated into child care settings. These programs have the potential to reach a wide range of high-risk toddlers who spend the majority of their day in child care. The objective of this study was to translate and evaluate the feasibility of the Social ABCs caregiver-mediated intervention program into a community child care setting by training front-line early childhood educators (ECEs). Twenty-two ECEs attended a workshop on early intervention and ASD, and six ECEs and one special needs resource consultant received hands-on intervention training. Nineteen participants completed a workshop quiz, with significant mean improvement of 22.26% from pre- to post-workshop. After 12 weeks of live coaching (4 weeks in one case), participants attained a high level of fidelity in implementing the intervention strategies (> 80%), which was maintained after a 3-month period of non-contact with the training team. Nine of ten specific strategies were mastered after the 12-week training period, with only one technique failing to reach a mean fidelity level of 75%. Findings reveal that the model of training front-line child care staff in a community child care setting is feasible using a relatively short-term training approach.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".