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Record 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 on OpenAlexaff
Marzieh Eghtesadi

Bibliographic record

VenueJournal of the American Geriatrics Society · 2020
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicineCoronavirus 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)

Abstract

fetched live from 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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.047
GPT teacher head0.400
Teacher spread0.353 · 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 designQualitative
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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Citations100
Published2020
Admission routes1
Has abstractyes

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