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Record W4415372727 · doi:10.1111/1460-6984.70145

Impact of Virtual Care on Speech‐Language Services

2025· article· en· W4415372727 on OpenAlexaffabout
Elizabeth M. Fitzpatrick, A. Grant

Bibliographic record

VenueInternational Journal of Language & Communication Disorders · 2025
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsCouncil of Ontario UniversitiesChild and Family Research InstituteAgricultural Research Institute of OntarioUniversity of Ottawa
Fundersnot available
KeywordsCoachingService (business)PerceptionIntervention (counseling)Service delivery frameworkPrimary care

Abstract

fetched live from OpenAlex

INTRODUCTION: COVID-19 impacted care delivery in rehabilitation services including speech-language pathology. The purpose of this study was to examine speech-language pathologists'(SLPs) perspectives on the effectiveness of virtual care delivered during the pandemic in Canada, their experiences with therapy delivered virtually and their views on future models of care. METHODS: We carried out a cross-sectional survey with SLPs in Canada who had delivered virtual services to children during the pandemic. The survey questions were based on information collected in a previous study involving focus group interviews with SLPs. The survey elicited responses related to SLPs' perception of effectiveness, their experiences with virtual care including perceived barriers and facilitators to implementing virtual care, and their vision for future speech-language services. Quantitative responses were compiled descriptively, and qualitative responses were reviewed and categorized. RESULTS: Seventy-five SLPs returned completed questionnaires. A majority (57.4%) reported that virtual care was very/extremely effective and 33.3% somewhat effective. The main barriers to providing virtual services were limited access to technology (family), limited workspace for the session at home, and limited availability of the caregiver for sessions. Services for children with complex developmental needs were viewed as more difficult to deliver virtually. Several positive aspects were highlighted including caregiver engagement in sessions and better work-life balance. The majority (84%) of SLPs indicated they would prefer to continue to use virtual care by adopting a hybrid model of service, while 8% of SLPs favored virtual care only and 8% in-person care only. CONCLUSIONS: Most SLPs reported that speech-language services via virtual care were effective. Practitioners indicated a preference for a hybrid model of care for post-pandemic services. Further research is needed to better identify what components of virtual care enhance services to better adapt service models in the future. WHAT THIS PAPER ADDS: What is already known on this subject Virtual care has been provided in speech-language pathology for many years but primarily in select circumstances for children living in remote areas. Speech-language care dramatically changed in many countries due to the required lock-down during the COVID-19 pandemic. What this paper adds to the existing knowledge This study provides updated information about the perceptions of effectiveness of virtual care for children based on the unplanned experiences of speech-language pathologists in Canada who were forced to rapidly implement a new service model. The findings suggest that overall practitioners adapted quickly and judged their services to be effective. Positive aspects of care included improved caregiver coaching, greater caregiver engagement and better work-life balance for practitioners. Primary barriers included the family's access to technology and the challenges of delivering care to children with complex needs. What are the potential or actual clinical implications for this work? This study supports the feasibility and effectiveness of speech-language care delivered virtually to children. This service model may result in improvements in both caregiving coaching and caregiver engagement. Most practitioners prefer shifting their post-pandemic services to hybrid models of care.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.750
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.402
Teacher spread0.393 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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