“I would have to walk around to find the best Wi-Fi connection…”: qualitatively exploring challenges associated with rapid rollout of telehealth in Canadian long-term care homes
Notice bibliographique
Résumé
Abstract Background Early in the COVID-19 pandemic, long-term care (LTC) homes in British Columbia, Canada, restricted visitation to ensure the safety of their residents against transmission of the novel coronavirus. As such, these LTC homes had to quickly implement a rapid rollout of telehealth services to maintain physician care for residents while avoiding the infection risk of in-person visits amidst lockdown measures. The abrupt transition from traditional in-person physician care to telehealth presented significant challenges. Investigating these challenges is pivotal to the development of strategies for sustained telehealth use for physician services in LTC homes. This analysis is part of a broader qualitative, utilization-focused evaluation study of telehealth services rapidly implemented for physician care in LTC homes within the Fraser Health Authority region of British Columbia. The evaluation has aimed to consider integral factors such as telehealth challenges, facilitators, preferences, and continued use. Semi-structured interviews and focus groups were conducted with 70 physicians, staff, residents, and family caregivers across 27 different LTC homes in the region. All interviews and focus groups were transcribed verbatim and were analyzed using a thematic approach to identify common barriers surrounding the rapid rollout of telehealth in LTC across relevant groups. Results From the data, four challenges were identified: connectivity challenges (e.g., inconsistent or no Wi-Fi or cellular connectivity), device challenges (e.g., lack of accessible devices and software issues), privacy challenges (e.g., lack of private space to support telehealth use), and informational challenges (e.g., lack of electronic medical record access). All challenges posed barriers to telehealth access for both care provider and recipient groups in LTC settings. Conclusions The challenges identified in this analysis are supported by existing literature, which is significant given the different contexts within which such research has been undertaken. Collectively, this knowledge base can support evidence-informed improvements to telehealth for physician care in LTC settings. Future research should capture the perspectives of diverse cultural groups, LTC residents with cognitive impairments, and those who provide and receive care in rural settings.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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