Equitable Aging Among Migrants: A Concept Analysis and Model Development for Transcultural Nursing Care
Bibliographic record
Abstract
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 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 | 0.035 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".