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Enregistrement W4298050430 · doi:10.2196/35864

Enabling Rural Telehealth for Older Adults in Underserved Rural Communities: Focus Group Study

2022· article· en· W4298050430 sur OpenAlexvenueno aff
Inga Hunter, Caroline Lockhart, Vasudha Rao, Beth Tootell, S.‐F. Wong

Notice bibliographique

RevueJMIR Formative Research · 2022
Typearticle
Langueen
DomaineMedicine
ThématiqueTelemedicine and Telehealth Implementation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésTelehealthFocus groupEnablingRural areaTelemedicineNursingService providerDigital healthHealth careRural healthBusinessMedicineService (business)PsychologyPolitical scienceMarketing

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Telehealth is often suggested to improve access to health care and has had significant publicity worldwide during the COVID-19 pandemic. However, limited studies have examined the telehealth needs of underserved populations such as rural communities. OBJECTIVE: This study aims to investigate enablers for telehealth use in underserved rural populations to improve access to health care for rural older adults. METHODS: In total, 7 focus group discussions and 13 individual interviews were held across 4 diverse underserved rural communities. A total of 98 adults aged ≥55 years participated. The participants were asked whether they had used telehealth, how they saw their community's health service needs evolving, how telehealth might help provide these services, and how they perceived barriers to and enablers of telehealth for older adults in rural communities. Focus group transcripts were thematically analyzed. RESULTS: The term telehealth was not initially understood by many participants and required an explanation. Those who had used telehealth reported positive experiences (time and cost savings) and were likely to use telehealth again. A total of 2 main themes were identified through an equity lens. The first theme was trust, with 3 subthemes-trust in the telehealth technology, trust in the user (consumer and health provider), and trust in the health system. Having access to reliable and affordable internet connectivity and digital devices was a key enabler for telehealth use. Most rural areas had intermittent and unreliable internet connectivity. Another key enabler is easy access to user support. Trust in the health system focused on waiting times, lack of and/or delayed communication and coordination, and cost. The second theme was choice, with 3 subthemes-health service access, consultation type, and telehealth deployment. Access to health services through telehealth needs to be culturally appropriate and enable access to currently limited or absent services such as mental health and specialist services. Accessing specialist care through telehealth was extremely popular, although some participants preferred to be seen in person. A major enabler for telehealth was telehealth deployment by a fixed community hub or on a mobile bus, with support available, particularly when combined with non-health-related services such as internet banking. CONCLUSIONS: Overall, participants were keen on the idea of telehealth. Several barriers and enablers were identified, particularly trust and choice. The term telehealth is not well understood. The unreliable and expensive connectivity options available to rural communities have limited telehealth experience to phone or patient portal use for those with connectivity. Having the opportunity to try telehealth, particularly by using video, would increase the understanding and acceptance of telehealth. This study highlights that local rural communities need to be involved in designing telehealth services within their communities.

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)0,004
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: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,164
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,110
Tête enseignante GPT0,461
Écart entre enseignants0,352 · 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'étudeQualitatif
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

Citations30
Publié2022
Routes d'admission1
Résumé présentoui

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