Barriers and facilitators to paediatric caregivers’ participation in virtual speech, language, and hearing services: A scoping review
Bibliographic record
Abstract
Purpose: Virtual care-related technologies are transforming the way in which health services are delivered. A growing number of studies support the use of virtual care in the field of audiology and speech-language pathology; however, there remains a need to identify and understand what influences caregiver participation within the care that is virtual and family-focused. This review aimed to identify, synthesize, and summarize the literature around the reported barriers and facilitators to caregiver participation in virtual speech/hearing assessment and/or intervention appointments for their child. Methods: A scoping review was conducted following the Joanna Briggs Institute manual for evidence synthesis. A search was conducted using six databases including MEDLINE, CINAHL, SCOPUS, ERIC, Nursing and Allied Health, and Web of Science to collect peer-reviewed studies of interest. Data was extracted according to a protocol published on Figshare, outlining a predefined data extraction form and search strategy. Results: A variety of service delivery models and technology requirements were identified across the 48 included studies. Caregiver participation was found to vary across levels of attendance and involvement according to eight categories: Attitudes, child behavioral considerations, environment, opportunities, provider-family relationship, role in care process, support, and technology. Conclusions: This review presents a description of the key categories reported to influence caregiver participation in virtual care appointments. Future research is needed to explore how the findings can be used within family-centered care models to provide strategic support benefiting the use and outcomes of virtual care.
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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 | 0.022 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".