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Record W4310336138 · doi:10.1177/20552076221139693

Technology implementation in care practices for community-dwelling older adults with mild cognitive decline: Perspectives of professional caregivers in Quebec and Brussels

2022· article· en· W4310336138 on OpenAlexaffabout
Samantha Dequanter, Iris Steenhout, Maaike Fobelets, M.‐P. Gagnon, Maxime Sasseville, Anne Bourbonnais, Anik Giguère, M.-A. Ndiaye, Audrey M. Lambert, Ellen Gorus, Ronald Buyl

Bibliographic record

VenueDigital Health · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsResearch CanadaInstitut Universitaire de Gériatrie de MontréalUniversité LavalUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean
FundersFonds Wetenschappelijk Onderzoek
KeywordsFacilitatorThematic analysisCognitive declineFocus groupContext (archaeology)GerontologyCognitionPopulationPsychologyQualitative researchPsychological interventionMedicineNursingDementiaPsychiatrySocial psychologySociologyDisease

Abstract

fetched live from OpenAlex

Objective: As worldwide population aging is accelerating, innovative technologies are being developed to support independent living among community-dwelling older adults with mild cognitive decline. However, the successful implementation of these interventions is often challenging. Until now, literature on implementation issues related to the specific context of older adults with mild cognitive decline is lacking and the few studies available do not focus specifically on the perspective of professional caregivers. Yet the perspective of these caregivers is important as they can be considered a key facilitator for technology implementation among this population. Therefore, this study was the first to examine technology implementation among community-dwelling older adults with mild cognitive decline from the broader perspective of professional caregivers. Methods: = 8). Braun and Clarke' method for thematic analysis, guided by a qualitative descriptive approach was applied to inductively identify themes from the data. Results: We identified factors influencing technology implementation in older adults with mild cognitive decline on three levels: an individual level (e.g., characteristics of older adults with mild cognitive decline and professional caregivers' attitude), an organizational level (e.g., lack of training among professional caregivers) and a level referring to the broader context (e.g., ethical considerations). Conclusions: This study contributes to the research gap in knowledge on the needs of professional caregivers to facilitate technology implementation among the population of older adults with cognitive decline. Future directions for research, practice, and policy are given, more specifically to improve knowledge among caregivers and on the development of decision support to retrieve safe and effective technologies that suit patient-centered care.

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)0.003
metaresearch head score (Gemma)0.008
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 score0.101
Threshold uncertainty score0.202

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.003
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.378
Teacher spread0.359 · 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

Citations8
Published2022
Admission routes2
Has abstractyes

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