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Record W2739512629 · doi:10.5014/ajot.2017.025817

Assistive Technology Addressing Safety Issues in Dementia: A Scoping Review

2017· review· en· W2739512629 on OpenAlexaff
Mireille Gagnon‐Roy, Annick Bourget, Stéphanie Stocco, Annie-Claude Lemieux Courchesne, Nicolas Kühne, Véronique Provencher

Bibliographic record

VenueAmerican Journal of Occupational Therapy · 2017
Typereview
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
Fundersnot available
KeywordsDementiaAssistive technologyCognitionPsychologyApplied psychologyComputer scienceMedicinePsychiatryHuman–computer interaction

Abstract

fetched live from OpenAlex

Safety is an issue for older adults with dementia because they are at risk for various incidents. Intelligent assistive technology (IAT) may mitigate risks while promoting independence and reducing the impact on the caregiver of supporting a relative with dementia. The aim of this scoping review was to describe IATs and to identify factors to consider when selecting one. A systematic search was performed of the scientific and gray literature published between 2000 and 2015. A total of 31 sources were included. Four types of IATs were identified as addressing safety issues in dementia: monitoring technologies, tracking and tagging technologies, smart homes, and cognitive orthoses. Characteristics of the device and ethical considerations emerged as key factors to consider when selecting one. IATs yield promising results but pose various challenges, such as adapting to the evolution of dementia. Further research on their actual impact is needed.

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)0.007
metaresearch head score (Gemma)0.032
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 score0.017
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.032
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0170.013
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.248
GPT teacher head0.556
Teacher spread0.308 · 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

Citations53
Published2017
Admission routes1
Has abstractyes

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Same venueAmerican Journal of Occupational TherapySame topicDementia and Cognitive Impairment ResearchFrench-language works237,207