Understanding the impact of digital technology on the well-being of older immigrants and refugees: A scoping review
Bibliographic record
Abstract
Background: The fast-paced development of digital technologies in the areas of social media, pet robots, smart homes, and artificial intelligence, among others, profoundly influence the daily lives of older adults. Digital technology can improve the well-being and quality of life of older adults, older immigrants and refugees who suffer migration-associated stress, loneliness, health and psychosocial challenges. Aims: The aim of this scoping review is to map out extant empirical literature that has examined the implication of digital technology among older refugees and immigrants. Methods: before the full-text review. The comprehensive database search yielded 4134 articles, of which 15 met the inclusion criteria. Results: The results of the review suggest that digital technology is essential to the well-being, quality of life of older immigrants and refugees, especially for maintaining and building new social support networks, navigating opportunities, coping with migration-induced stress through e-leisure, and staying connected to their culture. The literature also revealed poor utilisation of digital technologies amongst older immigrants and refugees, suggesting barriers to access. Conclusion: The study concluded by highlighting the need for more research and interventions that focus on multiple strategies, including education for increased access to and utilisation of digital technology to ensure that more older migrants can benefit from the advantages of digital technology in a safe way.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 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".