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Record W2336747671 · doi:10.1177/1471301215622839

Do physical environmental changes make a difference? Supporting person-centered care at mealtimes in nursing homes

2016· article· en· W2336747671 on OpenAlexaff
Habib Chaudhury, Lillian Hung, Tiana Rust, Sarah Wu

Bibliographic record

VenueDementia · 2016
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of AlbertaVancouver Coastal HealthSimon Fraser University
Fundersnot available
KeywordsDementiaNursingPsychologyNursing homesMedicineGerontology

Abstract

fetched live from OpenAlex

Purpose Drawing on therapeutic physical environmental design principles and Kitwood's theoretical view of person-centered care, this study examined the impact of environmental renovations in dining spaces of a long-term care facility on residents' mealtime experience and staff practice in two care units. Method The research design involved pre- and post-renovation ethnographic observations in the dining spaces of the care units and a post-renovation staff survey. The objective physical environmental features pre- and post-renovations were assessed with a newly developed tool titled Dining Environment Audit Protocol. We collected observational data from 10 residents and survey responses from 17 care aides and nurses. Findings Based on a systematic analysis of observational data and staff survey responses, five themes were identified: (a) autonomy and personal control, (b) comfort of homelike environment, (c) conducive to social interaction, (d) increased personal support, and (e) effective teamwork. Implications Although the physical environment can play an influential role in enhancing the dining experience of residents, the variability in staff practices reveals the complexity of mealtime environment and points to the necessity of a systemic approach to foster meaningful culture change.

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)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.003
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
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 head0.040
GPT teacher head0.356
Teacher spread0.316 · 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 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

Citations68
Published2016
Admission routes1
Has abstractyes

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