Ambiguous Loss Among Aging Migrants: A Concept Analysis- and Nursing Care-Oriented Model
Bibliographic record
Abstract
Introduction: Ambiguous loss is a profound yet underexplored phenomenon in the lives of aging migrants. Older adults who have experienced migration often face disruptions to their sense of belonging, identity, and continuity across borders. These losses are compounded by aging, health challenges, and social isolation. Despite its significance, ambiguous loss among aging migrants has not been conceptually analyzed in depth, limiting the development of culturally responsive care practices. Aim: This concept analysis aimed to identify the defining attributes of ambiguous loss among aging migrants and to develop a conceptual definition that enhances our understanding of the phenomenon and informs future research and practice. Method: Walker and Avant’s eight-step concept analysis framework was applied to examine the concept of ambiguous loss in the context of aging migrants. A systematic keyword search was conducted across four databases (CINAHL, Medline, SCOPUS, PsycINFO), Google Scholar, and relevant gray literature, covering the years of 2010–2024. Covidence software supported the screening process. From 367 records identified, 146 underwent full-text review, and 74 met inclusion criteria. The analysis drew on literature synthesis, case exemplars, antecedents, consequences, and empirical referents. This review followed PRISMA (2020) reporting guidelines. Results: Four defining attributes of ambiguous loss among aging migrants were identified: (a) physical, social, and emotional loss; (b) displacement and loss of homeland; (c) erosion of social identity and agency; and (d) cultural and transnational bereavement. A conceptual definition emerged, describing ambiguous loss as a multifaceted experience of disconnection, intensified by aging, illness, economic hardship, and social isolation. The analysis also highlighted antecedents such as forced migration and health decline, as well as consequences including diminished well-being, resilience challenges, and barriers to integration. Conclusions: Ambiguous loss among aging migrants is a complex construct encompassing intertwined physical, social, and cultural dimensions of loss. This conceptual clarity provides a foundation for developing culturally responsive care models that promote adaptation, resilience, and social inclusion among older migrants.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".