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Record W4408522924 · doi:10.1177/08980101251323012

Resourceful Aging Among Migrants and Refugees: A Concept Analysis and Model Development for Holistic Nursing Care

2025· article· en· W4408522924 on OpenAlexaff
Areej Al‐Hamad, Yasin M. Yasin, Rezwana Rahman, Victoria Hayrabedian, Kateryna Metersky

Bibliographic record

VenueJournal of Holistic Nursing · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of New BrunswickToronto Metropolitan University
Fundersnot available
KeywordsRefugeeEmpowermentSocial connectednessPsychological resilienceSociologyAdaptation (eye)PsychologyNursingSocial psychologyMedicinePolitical science

Abstract

fetched live from OpenAlex

Purpose: This concept analysis aims to conceptually define resourceful aging among migrants and refugees and develop a model integrating holistic nursing and healthcare practices to enhance their well-being. Methods: Using Walker and Avant’s methodology for concept analysis, we conducted a thorough and comprehensive literature search. Findings: Resourceful aging among migrants and refugees is characterized by adaptability, social connectedness, resilience, and resource navigation. Key antecedents include access to basic needs and services, social and community support, cultural familiarity and integration, legal status stability, and culturally competent services. Consequences include improved well-being, social integration, reduced reliance on social services, intergenerational bonds, empowerment, and cultural identity preservation. Conclusions: Resourceful aging among migrants and refugees involves adapting to aging in a new country by embracing changing circumstances and utilizing available resources, enabling well-being, personal agency, and resilience. This process preserves cultural heritage while adapting to new environments, balancing adaptation with identity. The integration of holistic nursing principles into resourceful aging among migrants and refugees can foster more inclusive and healthy communities.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score0.809

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.040
GPT teacher head0.410
Teacher spread0.370 · 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.

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

Citations3
Published2025
Admission routes1
Has abstractyes

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