Strengthening Personal Capacity of Older Adults in Culturally Diverse Context
Bibliographic record
Abstract
Abstract The critical interplay of cultural diversity, digital access, lifelong learning, and personal capacity is a key focus, particularly for older adults in multicultural settings. These complex factors across diverse landscapes demand exploration to address challenges and enhance well-being. This symposium presents five studies examining personal capacity, social resilience, mental health, digital inclusion, and community empowerment among aging populations, spotlighting South Asian elders in Hong Kong, racialized immigrants in Canada, older adults in China, vulnerable seniors in Hong Kong’s digital context, and older Chinese immigrants in Canada. These studies underscore the need for culturally congruent, tech-savvy, and education-driven interventions. They highlight challenges in social connectivity, community engagement, mental health, digital literacy, and discrimination, alongside the power of intergenerational ties and learning. Critical elements like cross-cultural understanding, resilience depth, access to community, digital, and educational resources, family dynamics, and unmet care needs significantly shape older adults’ lives. The research strongly supports culturally attuned, digitally inclusive, and learning-focused policies and practices. It emphasizes empowerment, resilience-building, mental health support, digital capacity enhancement, and community advocacy tailored to diverse identities and needs. These findings are vital for crafting targeted interventions, policies, and frameworks to overcome unique barriers faced by ageing individuals in culturally diverse, digitally advancing, and socially evolving contexts worldwide. International Aging and Migration Interest Group Sponsored Symposium
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".