Families' Experiences in the Virtual Hanen <i>More Than Words</i> Program During the COVID-19 Pandemic
Bibliographic record
Abstract
Purpose: The COVID-19 pandemic required most pediatric rehabilitation programs to shift to a virtual delivery format without the benefits of evidence to support this transition. Our study explored families' experiences participating virtually in More Than Words , a program for parents of autistic children, with the goal of generating new evidence to inform both virtual service delivery and program development. Method: Twenty-one families who recently completed a virtual More Than Words program participated in a semistructured interview. The interviews were transcribed and analyzed in NVivo using a top-down deductive approach that referenced a modified Dynamic Knowledge Transfer Capacity model. Results: Six themes capturing families' experiences with different components of virtual service delivery were identified: (a) experiences participating from home, (b) accessing the More Than Words program, (c) delivery methods and program materials, (d) the speech-language pathologist–caregiver relationship, (e) new skills learned, and (f) virtual program engagement. Conclusions: Most participants had a positive experience in the virtual program. Suggested areas for improvement included the time and length of intervention sessions and increasing social connections with other families. Practice considerations related to the importance of childcare during group sessions and having another adult to support the videorecording of parent–child interactions. Clinical implications include suggestions for how clinicians can create a positive virtual experience for families. Supplemental Material: https://doi.org/10.23641/asha.22177601
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".