Social Networks and Support to Older People in Refugee Situation in Western Countries
Bibliographic record
Abstract
Background: Forced migration of humans as a result of conflict continues to be a global problem. Many of the refugees displaced and made vulnerable by conflict induced forced migration are older adults. These older adults may lose their social networks and support as a result of the conflicts leading to migration and be unable to recreate them, making them more vulnerable. This review aims to describe the social network and support situation of older adults in refugee situation as presented in global literature. Methods/Design: The five steps of Arksey and O’Malley’s (2005) framework to search multiple databases from inception till June 2021 will be followed. MeSH terms and keywords, e.g., “older refugees”, “refugees”, and “social network”, “social support”, will be adopted for the following databases: SocINDEX, PsychINFO, Social Work Abstracts, Sociology Abstracts, Social Services Abstracts, Web of Science and/or Scopus, Canadian electronic library. Citations will be screened (title/abstract and full text) using predefined inclusion and exclusion criteria. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) will be used to describe the process of data inclusion and exclusion. Discussion: This review will reveal gaps in the provision of social support to older refugees and inform policy development for the improvement of support to older refugees.
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 | 0.006 | 0.036 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".