MétaCan
Menu
Back to cohort
Record W3161975732 · doi:10.1177/01640275211012652

The Relationships of Nursing Home Culture Change Practices With Resident Quality of Life and Family Satisfaction: Toward a More Nuanced Understanding

2021· article· en· W3161975732 on OpenAlexaff
Yinfei Duan, Christine Mueller, Fang Yu, Kristine Mc Talley, Tetyana Shippee

Bibliographic record

VenueResearch on Aging · 2021
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEmpowermentOperationalizationCulture changeNursingPsychologyOrganizational cultureQuality of life (healthcare)Quality (philosophy)GerontologyMedicinePublic relationsSociologyPolitical science

Abstract

fetched live from OpenAlex

Transforming nursing homes (NHs) from restrictive institutions to person-centered homes, referred to as NH culture change, is complex and multifaceted. This study, based on a survey of administrators in Minnesota NHs ( n = 102), tested the domain-specific relationships of culture change practices with resident quality of life (QOL) and family satisfaction, and examined the moderating effect of small-home or household models on these relationships. The findings revealed that culture change operationalized through physical environment transformation, staff empowerment, staff leadership, and end-of-life care was positively associated with at least one domain of resident QOL and family satisfaction, while staff empowerment had the most extensive effects. Implementing small-home and household models had a buffering effect on the positive relationships between staff empowerment and the outcomes. The findings provide meaningful implications for designing and implementing NH culture change practices that best benefit residents’ QOL and improve family satisfaction.

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)0.004
metaresearch head score (Gemma)0.008
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.710
GPT teacher head0.592
Teacher spread0.118 · 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

Citations18
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueResearch on AgingSame topicGeriatric Care and Nursing HomesFrench-language works237,207