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Enregistrement W4415372727 · doi:10.1111/1460-6984.70145

Impact of Virtual Care on Speech‐Language Services

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

Notice bibliographique

RevueInternational Journal of Language & Communication Disorders · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueTelemedicine and Telehealth Implementation
Établissements canadiensCouncil of Ontario UniversitiesChild and Family Research InstituteAgricultural Research Institute of OntarioUniversity of Ottawa
Organismes subventionnairesnon disponible
Mots-clésCoachingService (business)PerceptionIntervention (counseling)Service delivery frameworkPrimary care

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,750
Score d'incertitude au seuil0,345

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,009
Tête enseignante GPT0,402
Écart entre enseignants0,393 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission2
Résumé présentoui

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