AGE-FRIENDLY INITIATIVES AND IMMIGRANT SENIORS: ADDRESSING SOCIAL ISOLATION USING TECHNOLOGY
Bibliographic record
Abstract
Of the eight dimensions of the World Health Organization’s “age-friendly” cities initiative, the communication domain highlights the importance of information about community events and services being available in formats that are ‘appropriate’ for older adults. Although older immigrants may face pervasive language barriers in the English-speaking communities they often reside in, various forms of technology show promise for bridging communication deficits and reducing social isolation. To better understand the role of technology for facilitating communication, a mixed-methods study consisting of surveys (n=100) and two focus groups led by native language speakers were conducted with Cantonese (n=6) and Mandarin speaking (n=19) seniors (65+) living in Chinatown, Toronto, Canada. The surveys used standardized scales for social participation to measure aspects of the seniors’ lived experiences including sense of belonging, availability and access to community/ healthcare services, frequency and quality of family and social interactions, and perceived personal well-being. Both quantitative and qualitative findings indicate that technology was favoured as a primary method of communication for immigrant seniors, and that this mode of communication can facilitate significant improvement in multiple domains of age-friendly initiatives including: social participation and inclusion, community support and health services, and transportation. To maximize the use of tangible and tech-based resources available to seniors (i.e., wearables, smartphones), communities should incorporate technology such as messaging apps or electronic translation services to instantly mobilize information and knowledge into accessible language and cultural formats. Circumventing language barriers with other seniors and service providers in the community, technology may promote greater social participation.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".