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Record W4254641823 · doi:10.11124/jbies-20-00264

Assistive technologies that support social interaction in long-term care homes: a scoping review

2021· review· en· W4254641823 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJBI Evidence Synthesis · 2021
Typereview
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsKellogg's (Canada)Nova Scotia Health AuthorityDalhousie University
Fundersnot available
KeywordsCINAHLLonelinessPsycINFOMEDLINEIndependent livingLong-term careSocial supportPsychologyHealth careGerontologyApplied psychologyNursingPsychological interventionMedicineSocial psychology

Abstract

fetched live from OpenAlex

ABSTRACT Objective: The objective of this review was to chart the literature on assistive technologies (excluding robots) that support social interaction of older adults in long-term care homes, and to advance a definition of socially assistive technologies. Introduction: Loneliness and social isolation have adverse effects on the health and well-being of older adults. Many long-term care homes provide recreational programming intended to entertain or distract residents, yet the evidence of their effectiveness is limited. Absent from the literature are comprehensive reviews of assistive technologies (other than robots) that are used to support social interaction in long-term care homes. Inclusion criteria: The review considered research studies as well as gray literature that included older adults (≥65 years) living in long-term care homes. The concept of interest was the use of assistive technologies (excluding robots) that support social interaction in long-term care homes. Methods: The databases were searched on June 26, 2019, and included CINAHL Full Text (EBSCO), MEDLINE (Ovid), PsycINFO (EBSCO), Sociological Abstracts (ProQuest), Embase (Elsevier), and Web of Science (Clarivate). The search for gray literature was conducted in ProQuest Dissertations and Theses Databases and across 11 websites during September and October 2019. The recommended JBI approach to study selection, data extraction, and data synthesis was used. Results: Twenty-five articles were included in this review, with comparable numbers of quantitative (n = 6), qualitative (n = 9), and mixed methods (n = 7) studies, with the remaining articles employing non-empirical designs (n = 3). Technologies were categorized as low (easily recognizable to everyone), medium (more electronics), or high (involves internet). Two studies reported on low-assistive technologies, including videotapes and the telephone. Medium-assistive technologies were identified in nine studies and included videophones; Nintendo Wii; tablet-based games; picture- and video-viewing tools; and CRDL (pronounced “cradle”), a special instrument that translates touch into sound. More than half (n = 14) of the included articles utilized high-assistive technologies, such as computer labs/kiosks, tablet-based applications, social media (eg, Facebook), videoconferencing, and multi-functional systems. Five studies measured whether assistive technologies had an impact on the quantity of long-term care residents’ social interaction levels. Qualitative themes were related to residents’ social connections and experiences after using various technologies. Four studies systematically incorporated a framework/model, and Social Structuration Theory was considered the most comprehensive. In the absence of a definition of socially assistive technologies, the definition advanced from this review is as follows: Socially assistive technologies are user-appropriate devices and tools that enable real-time connectivity to enhance social interaction. Conclusions: Included literature reported the benefits of technology use, with considerable variability in engagement and no cost estimates. We recommend that future research continue to advance our definition of socially assistive technologies, make promising assistive technologies available in long-term care homes after studies are completed, report the costs of assistive technologies, and include participants with dementia and culturally and linguistically diverse backgrounds.

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.

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)0.002
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.882
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0020.001
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 head0.067
GPT teacher head0.422
Teacher spread0.355 · 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