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Record W4403375124 · doi:10.1080/26892618.2024.2409399

The Impact of Physical Environment on Residents’ Well-Being and Staff Care Practice in Dementia Care Homes in Canada and the Netherlands

2024· article· en· W4403375124 on OpenAlexaffabout
Sook Young Lee, Lillian Hung, Joey Wong, Lily Haopu Ren, Karen Lok Yi Wong, Amanda Yee

Bibliographic record

VenueJournal of Aging and Environment · 2024
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsMcGill UniversityUniversity of British Columbia
FundersNational Research Foundation of Korea
KeywordsDementiaGerontologyMedicineResidential careNursingAged carePsychologyFamily medicine

Abstract

fetched live from OpenAlex

This qualitative study examines how the physical environment affects residents’ well-being and staff’s practice in dementia care homes. Staff members in four care homes participated in focus groups in Canada and the Netherlands. Thematic analysis generated five themes: (i) promoting autonomy and social engagement, (ii) creating a home-like atmosphere, (iii) managing sensory stimulation, (iv) fostering a sense of connectedness, and (v) facilitating a stress-free work environment The study highlights the physical environment’s profound impact on residents’ well-being and staff care practice.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.260
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.302
Teacher spread0.295 · 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 designObservational
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

Citations6
Published2024
Admission routes2
Has abstractyes

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