Immigrant Older Adults’ Experiences of Aging in Place and Their Neighborhoods: A Qualitative Systematic Review
Bibliographic record
Abstract
Engaging in one's neighborhood fosters independence, promotes social connectedness, improves quality of life, and increases life expectancy in older adults. There is a lack of evidence synthesis on immigrant older adults' neighborhood perceptions and experiences, essential for addressing neighborhood-level influences on aging in place. This study systematically synthesizes qualitative evidence on immigrant older adults' perceptions and experiences of their neighborhoods. A comprehensive search was conducted from inception to 5 April 2023, in multiple databases. This review considered studies including immigrant older adults aged ≥60 years, included studies from any country where the neighborhood was the focus, and only considered qualitative data while excluding review studies, theoretical publications, and protocols. Eligible studies were appraised using the JBI critical appraisal checklist for qualitative research. The Joanna Briggs Institute meta-aggregation approach was used to synthesize findings, and the ConQual approach established confidence in the synthesis. A total of 30 studies were included. Most studies were conducted in North America and explored phenomena such as aging in place, social capital, social cohesion, sense of community, and life satisfaction. Key contextual factors were walkable safe access to social spaces, accessible transportation to amenities, social cohesion with neighbors, and pre-migration neighborhood experiences. Immigrant older adults have varied experiences related to their sense of belonging and social cohesion. Factors such as racial discrimination, feeling unsafe, and social isolation contributed to negative perceptions. This review highlights the need for inclusive neighborhoods that align with the needs and values of immigrant older adults aging in place.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".