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Record W4413109296 · doi:10.1177/10436596251359129

Equitable Aging Among Migrants: A Concept Analysis and Model Development for Transcultural Nursing Care

2025· article· en· W4413109296 on OpenAlexafffund
Areej Al‐Hamad, Yasin M. Yasin, Sepali Guruge, Kateryna Metersky, Lu Wang, Cristina Catallo, Hasina Amanzai, Zhixi Cecilia Zhuang, Rezwana Rahman

Bibliographic record

VenueJournal of Transcultural Nursing · 2025
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of New BrunswickToronto Metropolitan University
FundersCanada First Research Excellence FundGovernment of Canada
KeywordsTranscultural nursingNursingGerontologyNursing carePsychologyMedicineHealth carePolitical science

Abstract

fetched live from OpenAlex

Introduction: Older migrants often face systemic barriers such as limited access to health care, social support, and culturally appropriate services, which hinder dignified aging. This concept analysis aims to define equitable aging among migrants and develop a model to guide transcultural nursing care. Methodology: Using Walker and Avant’s concept analysis method, a systematic search following PRISMA-ScR guidelines yielded 351 records. After deduplication, 349 titles and abstracts were screened, 138 full-text articles were reviewed, and 68 studies were included in the final analysis. Results: Three defining attributes of equitable aging were identified: fair and just provision of health and social care; elimination of systemic barriers; and inclusive culturally responsive care. A conceptual model was developed, aligning equitable aging with key principles of transcultural nursing. Discussion: This concept analysis offers greater conceptual clarity on equitable aging among migrants and identifies defining attributes that may inform future development of theoretical models and culturally responsive practices.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.030
GPT teacher head0.396
Teacher spread0.366 · 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 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

Citations2
Published2025
Admission routes2
Has abstractyes

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