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Record W4205364495 · doi:10.2196/31162

Technology-Mediated Enrichment in Aged Care: Survey and Interview Study

2022· article· en· W4205364495 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJMIR Aging · 2022
Typearticle
Languageen
FieldPsychology
TopicSocial Robot Interaction and HRI
Canadian institutionsnot available
Fundersnot available
KeywordsData collectionAged careQualitative researchHealth careInterviewMEDLINE

Abstract

fetched live from OpenAlex

BACKGROUND: Digital technologies such as virtual reality (VR), humanoid robots, and digital companion pets have the potential to provide social and emotional enrichment for people living in aged care. However, there is currently limited knowledge about how technologies are being used to provide enrichment, what benefits they provide, and what challenges arise when deploying these technologies in aged care settings. OBJECTIVE: This study aims to investigate how digital technologies are being used for social and emotional enrichment in the Australian aged care industry and identify the benefits and challenges of using technology for enrichment in aged care. METHODS: A web-based survey (N=20) was distributed among people working in the Australian aged care sector. The survey collected information about the types of technologies being deployed and their perceived value. The survey was followed by semistructured interviews (N=12) with aged care workers and technology developers to investigate their experiences of deploying technologies with older adults living in aged care. Survey data were analyzed using summary descriptive statistics and categorizing open-ended text responses. Interview data were analyzed using reflexive thematic analysis. RESULTS: The survey revealed that a range of commercial technologies, such as VR, tablet devices, and mobile phones, are being used in aged care to support social activities and provide entertainment. Respondents had differing views about the value of emerging technologies, such as VR, social robots, and robot pets, but were more united in their views about the value of videoconferencing. Interviews revealed 4 types of technology-mediated enrichment experiences: enhancing social engagement, virtually leaving the care home, reconnecting with personal interests, and providing entertainment and distraction. Our analysis identified 5 barriers: resource constraints, the need to select appropriate devices and apps, client challenges, limited staff and organizational support, and family resistance. CONCLUSIONS: This study demonstrates that technologies can be used in aged care to create personally meaningful enrichment experiences for aged care clients. To maximize the effectiveness of technology-mediated enrichment, we argue that a person-centered care approach is crucial. Although enrichment experiences can be created using available technologies, they must be carefully selected and co-deployed with aged care clients. However, significant changes may be required within organizations to allow caregivers to facilitate individual technology-based activities for enrichment.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.054
GPT teacher head0.395
Teacher spread0.341 · 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