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Record W4390229569 · doi:10.1080/01621424.2023.2290708

Understanding the home environment of older adults living with dementia: A scoping review of assessment tools

2023· review· en· W4390229569 on OpenAlexafffund
Cindy Louis-Delsoin, Alicia Ruiz-Rodrigo, Jacqueline Rousseau

Bibliographic record

VenueHome Health Care Services Quarterly · 2023
Typereview
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal
FundersUniversité de MontréalSocial Sciences and Humanities Research Council of CanadaMitacsAlzheimer Society
KeywordsDementiaGerontologyMedicineActivities of daily livingAging in placePhysical therapyDiseasePathology

Abstract

fetched live from OpenAlex

Rigorous assessments to better understand the person-environment interaction are essential to comprehend how neurocognitive disorders influence in-home functioning of older people living with dementia. No recent synthesis identifies validated instruments targeting the human (e.g. caregivers) and nonhuman (e.g. objects) elements of the home environment interacting with this population and used with the perspective of aging in place. Consequently, following Arksey and O’Malley’s (2005) scoping review method, 2,182 articles were identified in six databases and in gray literature. Two reviewers independently selected 23 relevant articles describing 19 validated assessment tools targeting elements of the home interacting with older people with dementia, namely: nonhuman environment (n = 13), human environment (n = 3), and person-environment interaction (n = 3). This overview highlights the scarcity of tools addressing the human environment and the person-environment interaction to foster sustainable at-home living for older people with neurocognitive disorders, demonstrating the need to incorporate new evidence-based, holistic methods into dementia home 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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.263
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
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 head0.071
GPT teacher head0.394
Teacher spread0.324 · 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.

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

Citations3
Published2023
Admission routes2
Has abstractyes

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