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Enregistrement W3015878996 · doi:10.1111/jgs.16478

Breaking Social Isolation Amidst COVID‐19: A Viewpoint on Improving Access to Technology in Long‐Term Care Facilities

2020· article· en· W3015878996 sur OpenAlexaff
Marzieh Eghtesadi

Notice bibliographique

RevueJournal of the American Geriatrics Society · 2020
Typearticle
Langueen
DomaineHealth Professions
ThématiqueGeriatric Care and Nursing Homes
Établissements canadiensCentre Hospitalier de l’Université de Montréal
Organismes subventionnairesnon disponible
Mots-clésMedicineCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakIsolation (microbiology)Social isolationSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Term (time)Long-term carePandemicBetacoronavirusMEDLINEIntensive care medicineMedical emergencyVirologyNursingPathologyBioinformaticsPsychiatryDiseaseInfectious disease (medical specialty)

Résumé

récupéré en direct d'OpenAlex

In addition to large-scale initiatives that have been implemented to prevent international spread of the coronavirus disease 2019 (COVID-19) pandemic, we should advocate for local action targeted at preventing the deleterious health effects of social isolation as a consequence of contingency measures.1 As a frontline physician involved in the care of older adults living in long-term care (LTC) facilities, I have witnessed profound isolation in this population; my patients have become prisoners in their one-bedroom homes, isolated from each other and the outside world. This extreme loneliness should raise concern as it is a known risk factor for poor health outcomes, including anxiety, depression, malnourishment, and worsening dementia.2, 3 One way of palliating social isolation would be to integrate technological advances in the care of populations at risk of being further secluded during health outbreaks. From the encounters I experienced, many older individuals in LTC facilities lacked access to common devices (eg, a smartphone that would have allowed them to “facetime” with family members). Such network-connected devices would also allow patients to freely access health information in the wake of the pandemic, in addition to giving them the opportunity for telecare. More advanced technology, for instance augmented reality, could as well prove beneficial in this patient population, by reducing the burden of frailty, increasing well-being and social participation, and thus promoting successful aging.4, 5 From the safety of the patients' own home, a device like a wireless virtual reality (VR) headset could provide the patient with immersive experiences, ranging from connecting with loved ones in a common simulated space to visiting environments not otherwise accessible (eg, a music concert or a nature expedition that could include interaction with virtual animals). For older patients isolated in LTC facilities, providing them with these technology-dependent amenities and social contacts could potentially decrease their sense of loneliness and increase their self-perceived health, similarly to the benefits seen with physically going outdoors.6, 7 These VR applications have shown positive impact, even in individuals with physical and cognitive impairment.8 Yet, none of these technologies was available in the centers I visited and making them available at present time would be impossible given the risk of disease exposure. I believe there are two reasons we have deprived the older population of technological advances: our inherent bias of assuming the aging population is passive and lacks the ability to learn, combined with the fact that this is a population who does not advocate for itself. However, as healthcare providers who strive to constantly improve the care we offer to our patients, we must update our practice of medicine and integrate assessment of technology use as part of the preventative healthcare we offer to vulnerable populations. We must structure our comprehensive assessment to dedicate time in asking our patients questions about concerns and barriers to accessing technology, while redirecting them to educational community resources when necessary. Whether it be in the context of social isolation to control a local gastroenteritis outbreak to a large-scale pandemic, giving older adults in LTC facilities the opportunity to access technology would enable them to maintain social contact and communication. Furthermore, it would allow physicians to virtually connect with these patients and increase frequency of medical contact. It is our duty as a society not only to address but also to prevent the long-term sequelae of a pandemic contingency planning, especially when health outcome entails experiencing invisible mental health illness. In regards to policy-making decisions and resource allocation, the success of making technology more accessible to the marginalized older population should not be measured solely on the outcome of avoiding acute care services; its benefits should rather be assessed with functional health as the focus of intervention, including measures of psychophysical well-being and life satisfaction.9 Higher-end immersive technologies could be installed as a private expense in a patient's room; they could also be made available in common recreational areas within a leisure and/or fitness room, provided by the LTC facility through support of government subsidies and incentives aimed at promoting health of its aging population. However, more popular interactive devices, such as smartphones and computer tablets, must be made available as an affordable commodity for the means of every patient at risk of social isolation, while providing all the necessary ergonomic adjustments to those with impaired physical and sensory function. Finally, just like the pharmaceutical industries should not be allowed to simply sell to the highest bidder during a pandemic, big tech corporations should be required to collaborate with governmental social initiatives to ensure access to technology for marginalized populations in times of public health crisis.10 Conflicts of interest to declare by author due to financial and personal relationships that could potentially bias this work: none. Dr Eghtesadi contributed entirely to drafting, revising, and approving the final manuscript for submission. Sponsor's Role: There is no sponsor role.

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)0,001
score de la tête « metaresearch » (Gemma)0,001
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: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,280
Score d'incertitude au seuil0,651

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,002
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
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 Opus0,047
Tête enseignante GPT0,400
Écart entre enseignants0,353 · 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

Citations100
Publié2020
Routes d'admission1
Résumé présentoui

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