Dementia caregiving in Chinese communities in high-income countries: Exploring unmet needs and experiences through a qualitative meta-synthesis
Bibliographic record
Abstract
Dementia presents significant global challenges, with care partners (family caregivers)-often family members-playing a central role in providing care and support, particularly in high-income countries. However, caregiving experiences are shaped by factors such as race, ethnicity, cultural values, and access to resources, necessitating inclusive research to better understand and address the diverse needs of care partners, particularly within ethnic minority groups like Chinese diaspora communities. We employed a meta-synthesis approach with several key steps, including defining the research question, evaluating primary studies, conducting meta-method and meta-theory analyses, and synthesizing findings. This study was framed within Arksey and O'Malley's scoping review framework and adhered to PRISMA-ScR guidelines. Ten articles were included. Cultural perceptions of dementia in Chinese communities often view it as a natural part of aging, leading to delays in diagnosis and treatment, while caregiving decisions are influenced by filial piety, financial constraints, and cultural norms. Care partners face barriers such as stigma, lack of awareness, and limited access to culturally sensitive support, leading to emotional and physical strain, often worsened by isolation and the challenges of navigating healthcare systems. Addressing cultural stigma, improving awareness, and enhancing access to culturally appropriate support are crucial for improving dementia care for Chinese communities.
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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 | 0.044 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".