Unlocking the Potential of Pediatric Virtual Care: An e-Delphi Study on a Virtual Caregiver Participation Framework in Audiology
Bibliographic record
Abstract
PURPOSE: Virtual service delivery models in audiology have become more accessible due to recent technological advancement and improved system-level uptake following COVID-19. Although current evidence identifies the benefits of virtual care to families with children who are d/Deaf or hard of hearing and supports its use in practice, this delivery model is still underutilized. This research aimed to gain consensus on an evidence-informed virtual caregiver participation framework developed from a scoping review of the communication sciences and disorders literature. METHOD: A two-round modified e-Delphi study was conducted to survey 26 knowledge users from four different countries with experience in virtual audiology care, including caregivers, audiologists, researchers, and organizational leaders. The study employed Delphi techniques, building from a scoping review to synthesize existing literature informing the knowledge gap, including online surveys and team discussions. Consensus was defined numerically (75% agreement) and by comparing and interpreting text-based responses. RESULTS: The resulting framework grouped nine categories of caregiver participation in virtual care according to three main readiness domains: core readiness (opportunities to participate, perceived value, and willingness to participate), engagement readiness (child capacity, family-provider relationship, and role in the care process), and structural readiness (environment for participation, support, and technology). CONCLUSION: This work adds novel contributions to the field, through the development of a framework for caregiver participation in virtual audiology care, that can be used to support family involvement and will guide clinical tool development and future research efforts.
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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 | 0.074 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| 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".