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Record W2800984779 · doi:10.4017/gt.2018.17.s.012.00

Expectations of stakeholders regarding technology in home care to optimise the functional autonomy of clients

2018· article· en· W2800984779 on OpenAlexaboutno aff
M. Couture, S. BANDALY, Sara-Valérie Giroux, Maxime Lussier, Carolina Bottari, Hélène Pigot, Guy Paré, Kelly Bouchard, Sébastien Gaboury, Nathalie Bier

Bibliographic record

VenueGerontechnology · 2018
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsAutonomyAssisted livingBusinessNursing homesNursingPsychologyMedicinePolitical science

Abstract

fetched live from OpenAlex

Purpose : In Quebec, administrators of home care are more and more open to offering smart environments as part of services to maintain at home individuals facing an important loss of autonomy. Nonetheless, the implementation of technology in the context of home care requires the involvement of other stakeholders including care providers, clients and their families. The influence of care providers on the implementation of technology within the healthcare system is documented (Cf. Reviews1 2). However, little information is available about the implementation of technology in the community as well as the perspective of the clients and their families. Consequently, stakeholders in home care may not have the same expectations as those working in an institutional setting. Acceptance of technology is not the only barrier to the implementation, other factors such as social, technical, and organizational context can play a role. \n \nMethod : Prior to implementing technology within home care services of an Integrated University Health and Social Services Centre in Montreal (Quebec), the purpose of this qualitative study was to evaluate the perspectives of multiple stakeholders in regards to: (1) facilitators and barriers to maintaining individuals at risk of self-neglect at home; and (2) expectations toward technology to optimise the functional autonomy of this clientele. lndividuals and group interviews were conducted with administrators (n = 2), head of services (n=S), care providers (n = 8), as well as clients at risk of \nself-neglect (n = 5) and their caregivers (n = 3). Data was analyzed using the approach of Miles, Huberman and \nSaldana. \n \nResults & Discussion : Perspectives of stakeholders were sometimes complementary but also divergent. On the one hand, the concept of perceived risk for the client was central to the decision-making process regarding the type of support needed to maintain the persan at home including technology. On the \nother hand, some of the clients expressed relatively no needs for services as they perceived themselves as functioning well at home while other stakeholders identified important problems. Overall, technology was \nexpected to fulfill two main functions: (1) obtaining additional data to support the decision-making process \nrelated to the type and frequency of support needed to maintain the client at home; and (2) supporting the \nautonomy of the client. In conclusion, these results suggest that the implementation of technology within home \ncare requires merging perspectives from multiple stakeholders to have a common understanding of the needs of the client and identify common objectives regarding the role of technology.

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)0.013
metaresearch head score (Gemma)0.023
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 score0.044
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.004
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.068
GPT teacher head0.339
Teacher spread0.271 · 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".

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Citations3
Published2018
Admission routes1
Has abstractyes

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