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Record W4200589672 · doi:10.2196/32442

COVID-19’s Influence on Information and Communication Technologies in Long-Term Care: Results From a Web-Based Survey With Long-Term Care Administrators

2021· article· en· W4200589672 on OpenAlexvenueno aff
Amy M. Schuster, Shelia R. Cotten

Bibliographic record

VenueJMIR Aging · 2021
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsnot available
Fundersnot available
KeywordsSocioemotional selectivity theoryTelehealthLong-term careInformation and Communications TechnologyPandemicBusinessCoronavirus disease 2019 (COVID-19)VideoconferencingMedicineNursingSocial mediaTelemedicineGerontologyHealth carePolitical scienceTelecommunications

Abstract

fetched live from OpenAlex

BACKGROUND: The prevalence of COVID-19 in the United States led to mandated lockdowns for long-term care (LTC) facilities, resulting in loss of in-person contact with social ties for LTC residents. Though information and communication technologies (ICTs) can be used by LTC residents to support their socioemotional needs, residents must have access to ICTs to use them. OBJECTIVE: This study explored ICT access and use in LTC facilities and how LTC facilities adapted to try to enhance social connections for their residents during the COVID-19 pandemic. METHODS: LTC administrators in South Carolina (United States) were invited to complete a web-based survey exploring ICT access and use in LTC facilities and whether access and use changed as a result of the COVID-19 pandemic. RESULTS: LTC administrators (N=70, 12 nursing homes [NHs], and 58 assisted living facilities [ALFs]) completed the web-based survey. Since March 2020, a total of 53% (37/70) of the LTC facilities have purchased ICTs for residents' use. ICTs have mainly been used for videoconferencing with family members (31/36, 86%), friends (25/36, 69%), and health care providers (26/36, 72%). NHs were 10.23 times more likely to purchase ICTs for residents' use during the COVID-19 pandemic than ALFs (odds ratio 11.23, 95% CI 1.12-113.02; P=.04). Benefits of ICT use included residents feeling connected to their family members, friends, and other residents. Barriers to ICT use included staff not having time to assist residents with using the technology, nonfunctional technology, and residents who do not want to share technology. CONCLUSIONS: Our results suggest that over half of the LTC facilities in this study were able to acquire ICTs for their residents to use during the COVID-19 pandemic. Additional research is needed to explore how residents adapted to using the ICTs and whether LTC facilities developed and adopted technology integration plans, which could help them be prepared for future situations that may affect LTC residents' engagement and communication opportunities, such as another pandemic.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.382
Teacher spread0.349 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

Quick stats

Citations31
Published2021
Admission routes1
Has abstractyes

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