Resourceful Aging Among Migrants and Refugees: A Concept Analysis and Model Development for Holistic Nursing Care
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".