Technology-Mediated Enrichment in Aged Care: Survey and Interview Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".