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Record W4388771913 · doi:10.1017/s0714980823000697

Staff’s Attitudes towards the Use of Mobile Telepresence Robots in Long-Term Care Homes in Canada

2023· article· en· W4388771913 on OpenAlexafffundabout
Mineko Wada, Joey Wong, Evangeline Tsevis, Jim Mann, Hideaki Hanaoka, Lillian Hung

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsUniversity of British Columbia
FundersVancouver Foundation
KeywordsPsychologyLong-term careNursingHealth careApplied psychologyRobotMedicineComputer science

Abstract

fetched live from OpenAlex

This cross-sectional study investigated staff's attitudes towards the use of mobile telepresence robots in long-term care (LTC) homes in western Canada. We drew on a Health Technology Assessment Core Model 3.0 to design a survey examining attitudes towards nine domains of mobile telepresence robots. Staff, including nurses, care staff, and managers, from two LTC homes were invited to participate. Statistical analysis of survey data from 181 participants revealed that overall, participants showed positive attitudes towards features and characteristics, self-efficacy on technology use, organizational aspects, clinical effectiveness, and residents and social aspects; neutral attitudes towards residents' ability to use technology, and costs; and negative attitudes towards safety and privacy. Participants who disclosed their demographic backgrounds tended to exhibit more positive attitudes than participants who did not. Content analysis of textual data identified specific concerns and benefits of using the robots. We discuss options for implementing mobile telepresence robots in LTC.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.259
Teacher spread0.235 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2023
Admission routes3
Has abstractyes

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Same venueCanadian Journal on Aging / La Revue canadienne du vieillissementSame topicTechnology Use by Older AdultsFrench-language works237,207