Aging Muslim immigrants transitioning from Muslim majority countries to Muslim minority countries: A scoping review addressing dynamics of occupation, place, and identity
Bibliographic record
Abstract
Immigration can challenge aging people’s health, social inclusion, and continued engagement in meaningful occupations, causing loss of and/or change in social status, relationships, and roles; all of which can be intimately tied to place and identity. Such losses may lead to depression, isolation, and can negatively affect quality of life. This scoping review aimed to better understand how aging Muslim immigrants re-establish and enact occupations and negotiate their identities across various places in the host country. Findings revealed the diverse ways aging Muslim immigrants made complex negotiations after migrating to an unfamiliar Muslim minority country to fulfill important roles, navigate ways to participate in meaningful occupations, and express their identities safely across places. The reviewed studies showed how this group was frequently confronted by challenges, including cultural and religious rejection, disruption in occupations, and structural barriers. However, many found ways to overcome these challenges through occupational engagement and social connectivity, which supported their integration process. Increased attention to the occupations of aging immigrants will allow researchers, service providers, and policy makers to make meaningful contributions to improve the lives of aging Muslim immigrants.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".