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Record W2331977091 · doi:10.1177/1471301214536910

Exploring staff perceptions on the role of physical environment in dementia care setting

2014· article· en· W2331977091 on OpenAlexaffabout
Sook Y. Lee, Habib Chaudhury, Lillian Hung

Bibliographic record

VenueDementia · 2014
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of British ColumbiaSimon Fraser University
FundersNational Research Foundation of Korea
KeywordsDementiaFocus groupRecreationNursingPerceptionPsychologyQuality (philosophy)Work (physics)MedicineBusinessDisease

Abstract

fetched live from OpenAlex

This study explored staff perceptions of the role of physical environment in dementia care facilities in affecting resident's behaviors and staff care practice. We conducted focus groups with staff (n = 15) in two purposely selected care facilities in Vancouver, Canada. Focus group participants included nurses, care aides, recreation staff, administrative staff, and family. Data analysis revealed two themes: (a) a supportive physical environment contributes positively to both quality of staff care interaction and residents' quality of life and (b) an unsupportive physical environment contributes negatively to residents' quality of life and thereby makes the work of staff more challenging. The staff participants collectively viewed that comfort, familiarity, and an organized space were important therapeutic resources for supporting the well-being of residents. Certain behaviors of residents were influenced by poor environmental factors, including stimulation overload, safety risks, wayfinding challenge, and rushed care This study demonstrates the complex interrelationships among the dementia care setting's physical environment, staff experiences, and residents' quality of life.

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)0.007
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 score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.318
Teacher spread0.275 · 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

Citations53
Published2014
Admission routes2
Has abstractyes

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