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Record W2022807833 · doi:10.1258/135763307780096195

A qualitative study of in-home robotic telepresence for home care of community-living elderly subjects

2007· article· en· W2022807833 on OpenAlexaff
Patrick Boissy, Hélène Corriveau, François Michaud, Daniel Labonté, M.-P. Royer

Bibliographic record

VenueJournal of Telemedicine and Telecare · 2007
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsHealth and Social Services Centre University Institute of Geriatrics of SherbrookeUniversité de Sherbrooke
Fundersnot available
KeywordsTelecareFocus groupGeriatricsUsabilityRespite careHealth careNursingQualitative researchTeleoperationTelehealthMultidisciplinary approachTelemedicineMedicinePsychologyMultimediaRobotComputer scienceHuman–computer interaction

Abstract

fetched live from OpenAlex

We examined the requirements for robots in home telecare using two focus groups. The first comprised six healthcare professionals involved in geriatric care and the second comprised six elderly people with disabilities living in the community. The concept of an in-home telepresence robot was illustrated using a photograph of a mobile robot, and participants were then asked to suggest potential health care applications. Interview data derived from the transcript of each group discussion were analyzed using qualitative induction based on content analysis. The analyses yielded statements that were categorized under three themes: potential applications, usability issues and user requirements. Teleoperated mobile robotic systems in the home were thought to be useful in assisting multidisciplinary patient care through improved communication between patients and healthcare professionals, and offering respite and support to caregivers under certain conditions. The shift from a traditional hospital-centred model of care in geriatrics to a home-based model creates opportunities for using telepresence with mobile robotic systems in home telecare.

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)0.003
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.722

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.056
GPT teacher head0.422
Teacher spread0.367 · 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 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

Citations98
Published2007
Admission routes1
Has abstractyes

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