ETHNIC VARIATIONS IN SOCIAL CAPITAL IN SOUTH ASIAN AND CHINESE OLDER ADULTS IN HONG KONG
Bibliographic record
Abstract
Abstract The role of social capital has gained a lot of attention as one of the determinants of wellbeing of aging adults. It serves the functions of connecting people with vital resources essential to improvement of health, promotion of social cohesion and networks, and enhancement of support received. Social capital could be influenced by a variety of individual and socio-cultural factors among racialized groups. This study aims to examine the variations in social capital in Chinese and South Asian aging adults in Hong Kong. A sample of 1,015 people aged 55 and above, consisting of Chinese (n=800) and South Asian participants (n=215), participated in a survey via telephone (Chinese) and face-to-face (South Asian) interview respectively. A 25-item World Bank’s Social Capital Assessment Tool was used for measuring six variables related to social capital including social participation, social support, social connection, trust, cohesion, and reciprocity, covering the structural and cognitive dimensions of social capital. A generalized linear model was performed to detect differences between the two groups after controlling for demographics. The aging Chinese people exhibited a stronger cohesion level than their South Asian counterparts, whereas the aging South Asians demonstrated higher levels of social participation, social support, social connection, trust, and reciprocity. The identified gaps further illustrate the socio-cultural differences between the groups, highlighting the importance of strategies for enhancing social inclusion and equity for racialized older people in the Chinese context.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".