Adopting a cultural lens on social capital of older Chinese and South Asians in Hong Kong: A comparative CFA study
Bibliographic record
Abstract
Abstract Social capital structures play an important role in addressing social and health care needs of older adults. Knowing culture provides the context for values, beliefs and behaviors, it is imperative to use an ethnically-specific approach to understand the complexity of social capital structures in a multi-national population. Ethnic diversity is a prominent characteristic among the aging population in Hong Kong, with the South Asian aged 55 increased at almost three times the rate of the local aged cohort. This study aimed to examined how social capital structures determined their care needs among these diverse group. This study conducted telephone survey to 800 Hong Kong older adults via random digital dialing. Another purposive sample of 215 South Asians were interviewed face-to-face. Confirmatory Factor Analysis revealed notable differences in social capital structures between the groups, with both models showing excellent reliability (South Asians: CFI=0.999, TLI=0.999, RMSEA=0.078; Hong Kong: CFI=0.991, TLI=0.987, RMSEA=0.077). South Asian older adults emphasized neighborhood cohesion, community interactions, and collective support, reflecting collectivist cultural values. Conversely, Hong Kong older adults focused on individual community participation, family-oriented support, and trust networks from family to neighbors, indicative of a more individualistic cultural orientation centered on personal achievements and family ties. These findings highlight the need for culturally responsive social policies. Programs for South Asian older adults should enhance neighborhood ties and collective support, while initiatives for Hong Kong older adults should strengthen familial networks and personalized social engagement. Future research should explore these distinctions to guide inclusive aging policies and community interventions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".