Do Caregiver Perceptions of the Virtual More Than Words® Program Differ Based on Autistic Children's Attributes?
Bibliographic record
Abstract
PURPOSE: More Than Words® (MTW) is a caregiver-mediated intervention program led by a speech-language pathologist (SLP) who teaches caregivers strategies to support their autistic child's early social communication and play development. The program includes group sessions composed of multiple families with children of varying profiles. We explored whether caregiver experiences and perceived outcomes of the virtual MTW program differed depending on the child's age and social communication stage. METHOD: form was analyzed both qualitatively and quantitatively, and a modified RE-AIM framework guided our analyses, including theme development. RESULTS: Child attributes did not appear to impact caregivers' experiences, but perceived child skill improvements varied by children's social communication stage. The majority of caregivers reported changes in how they interact with their child. Four themes emerged: (a) perceived child skill improvements differed by social communication stage, (b) caregivers gained new knowledge and strategies regardless of child attributes, (c) SLPs effectively managed families' individual needs, and (d) program components were appropriate for a variety of families. CONCLUSIONS: Findings suggest that the content taught in the MTW program was relevant for a variety of children, including those beyond the program's intended age of 5 years and under. Grouping families of children with varying profiles does not appear to negatively influence caregivers' experiences or perceived outcomes. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.25237009.
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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 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".