Understanding the Impact of Digital Technology on the Well-being of Older Immigrants and Refugees: A Scoping Review Protocol
Bibliographic record
Abstract
Abstract Although, scholarly reports show that some older adults utilise digital technology to enhance their well-being, a significant number of older adults are digitally alienated. This is complicated for older immigrants and refugees, whose situations present a peculiar challenge, requiring digital technology for improved quality of life. Hence, this scoping review seeks to understand the impact of digital technology on the well-being of older immigrants and refugees. Arksey & O’Malley’s five-stage framework will guide the review. The following databases: Social Work Abstract, Social Service Abstracts, Abstracts of Social Gerontology, International Bibliography of the Social Sciences (IBSS), etc., will be searched. Citations from the databases will be exported to Zotero to eliminate duplicate articles. These citations will be subjected to two screening levels. For the first screening, citations will be exported from Zotero to Rayyan QCRI© for title and abstract review. After that, all the authors will conduct full-text reviews of all articles included. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) will be utilised in describing and documenting the inclusion and exclusion process. This scoping review will engender an improved understanding of the implications of digital technology on the well-being of older 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 | 0.096 | 0.097 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.073 | 0.018 |
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".