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Record W4403807038 · doi:10.2196/52834

Exploring Older Adults’ Perspectives on Digital Home Care Interventions and Home Modifications: Focus Group Study

2024· article· en· W4403807038 on OpenAlexaffvenueabout
Mohamed-Amine Choukou, Jasem Banihani, Sarah Azizkhani

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPreprintGerontechnologyGerontologyPsychologyEngineeringComputer scienceMedicineWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: Emerging gerontechnology seeks to enable older adults (OAs) to remain independently and safely in their homes by connecting to health and social support and services. There are increasing attempts to develop gerontechnology, but successful implementations are more likely limited because of the uncertainty of developers about the needs and priorities of OAs. As the global population ages, the challenges faced by older OAs in maintaining independence and well-being within their homes have become increasingly important. With the proportion of OAs expected to triple by 2068, addressing the needs of this demographic has become a pressing social and public health priority. OAs often encounter various challenges related to physical, cognitive, and social well-being, including reduced mobility, memory impairments, and social isolation, which can compromise their ability to age in place and maintain a high quality of life. OBJECTIVE: The goals of this qualitative research study are to (1) determine the best strategies for promoting aging well in the community with the support of gerontechnology, (2) establish the top priorities for implementing gerontechnology with OAs and their families, and (3) create a road map for the creation and application of gerontechnology for aging well in Manitoba. METHODS: A total of 14 OAs participated in a qualitative research study conducted through a coconstruction workshop format, including a presentation of novel research facilities and a demonstration of research and development products. This activity was followed by an interactive discussion focused on revisiting the ongoing research and innovation programs and planning for a new research and innovation agenda. The workshop contents, notes, and recorded conversation underwent a data-driven inductive analysis. RESULTS: Emerging themes included home design, accessibility, and safety for OAs, particularly those with memory impairments. The participants also underlined the need for digital reminders and ambient technologies in current homes as a priority. Participants stressed the importance of including OAs in gerontechnology development programs and the need to consider dignity and independence as the guiding values for future research. CONCLUSIONS: This study presents a tentative road map for the development of gerontechnology in Manitoba. The main principles of our road map are the inclusion of OAs as early as possible in gerontechnology development and the prioritization of independence and dignity. Applying these principles would contribute to combatting digital ageism and the marginalization of OAs in technology development because of the perceived lack of technological skills and the stereotypes associated with this presumption.

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)0.015
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0080.004
Scholarly communication0.0030.004
Open science0.0010.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.129
GPT teacher head0.427
Teacher spread0.298 · 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 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".

Quick stats

Citations1
Published2024
Admission routes3
Has abstractyes

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