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Record W4410929958 · doi:10.1093/geroni/igaf034

A Mixed-Methods Scoping Review of Innovative Long-term Care Facility Design and Associated Outcomes

2025· review· en· W4410929958 on OpenAlexaff
Elizabeth Pywell, Katherine M. Ottley, Azin Dolatabadi, Kayley Lawrenz, Heather Ward, Abigail Wickson‐Griffiths, Paulette V. Hunter

Bibliographic record

VenueInnovation in Aging · 2025
Typereview
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of ReginaUniversity of Saskatchewan
Fundersnot available
KeywordsLong-term careScale (ratio)Quality (philosophy)Quality of life (healthcare)Term (time)NursingAssisted livingPsychologyMedicineResearch designHealth careGerontologyGeography

Abstract

fetched live from OpenAlex

Background and Objectives: As people live to late older adulthood, their reliance on disability supports and services increases. While these supports and services can often be provided at home, many people spend a period of their lives in long-term care, and the quality of long-term care environments is of great significance to those who make this transition and to those who support it. The objective of this study was to survey the range of design innovations in long-term care and to consider outcomes for residents, family caregivers, employees, and healthcare organizations. Research Design and Methods: To achieve these goals, we conducted a systematic scoping review and analyzed results using a convergent segregated mixed-methods approach. We summarized 75 articles on the topic of long-term care home building design by classifying structural design features and associated outcomes. Results: ). A wide range of potential positive outcomes were identified for residents, families, and staff. These outcomes included outcomes of central significance for long-term care, including improved quality of life, improved family satisfaction, and improved staff engagement in work. Discussion and Implications: Based on these results, environmental design is a critical contributor to long-term care quality.

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)0.005
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.574
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.008
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 head0.164
GPT teacher head0.540
Teacher spread0.376 · 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

Citations4
Published2025
Admission routes1
Has abstractyes

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