MétaCan
Menu
Back to cohort
Record W3096266399 · doi:10.1080/17483107.2020.1844320

What facilitates the acceptance of technology to promote social participation in later life? A systematic review

2020· review· en· W3096266399 on OpenAlexaff
Laurence Benoit-Dubé, Eudia Kévine Jean, Melissa Arriola Aguilar, Ana-Marcia Zuniga, Nathalie Bier, Mélanie Couture, Maxime Lussier, Xanthy Lajoie, Patrícia Belchior

Bibliographic record

VenueDisability and Rehabilitation Assistive Technology · 2020
Typereview
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsUniversité de MontréalInstitut Universitaire de Gériatrie de MontréalMcGill University
Fundersnot available
KeywordsCINAHLFacilitatorHealth technologyPsychologyQuality of life (healthcare)Critical appraisalMEDLINEPsychological interventionSocial mediaIntervention (counseling)Medical educationHealth careGerontologyPublic relationsApplied psychologyMedicineSocial psychologyPolitical scienceAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Social participation is an important aspect of health and well-being across the lifespan, but older adults might encounter some barriers, which has been highlighted in the current Covid-19 pandemic situation, where technology has become the primary way to maintain contact with family and friends. In fact, technology can serve both as a facilitator and barrier to social participation in later life, and this issue needs to be further understood. AIM: To identify the barriers and facilitators encountered by older adults in using technology to promote social participation. METHODS: A systematic review was conducted. Studies were included if they were peer-reviewed, written in English or French, included participants 50 years or older, included technology to promote social participation, and reported potential barriers or facilitators regarding such technologies. Four databases were included: MEDLINE, CINAHL, PsychINFO and, ERIC. Each study was reviewed by two independent reviewers. The quality of the study was appraised using the Crowe Critical Appraisal Tool. RESULTS: Seventeen studies were included in this report. Four main themes emerged from the data: perceived benefits of the technology, self-confidence and knowledge about using the technology efficiently and safely, affordability of the technology, and ability of the technology to adapt to the physical and cognitive declines in later life. CONCLUSION: These findings can help health care professionals to make better decisions when deciding to recommend technology for their older clients.IMPLICATIONS FOR REHABILITATIONAcceptance of technology to promote social participation in later life is a multi-complex process. There is no "one size fits all" approach, a person-centered intervention must be used.When introducing new technologies, using an adapted/tailored training approach could potentially increase self-efficacy in using technology.Rehabilitation professionals' misconceptions concerning the use of technology in later life can be a barrier to acceptance. It's important to be aware of our own believes and attitudes in this context.

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)0.019
metaresearch head score (Gemma)0.099
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.019
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.099
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.007
Bibliometrics0.0120.012
Science and technology studies0.0010.001
Scholarly communication0.0050.005
Open science0.0020.002
Research integrity0.0030.002
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 head0.029
GPT teacher head0.382
Teacher spread0.354 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations18
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueDisability and Rehabilitation Assistive TechnologySame topicTechnology Use by Older AdultsFrench-language works237,207