Palliative and end-of-life care for Chinese immigrants: experiences of family caregivers
Bibliographic record
Abstract
Though Chinese immigrants constitute one of the largest immigrant groups in Canada, little research has been conducted to explore palliative and end-of-life care for this community. The purpose of this research was to learn about the lived experiences of Chinese family caregivers who have provided palliative and end-of-life care to their loved ones. This qualitative study adopted hermeneutic phenomenology to understand palliative and end-of-life care for Chinese immigrants from the perspective of their family caregivers. Seven Chinese immigrants living in the Greater Toronto Area (GTA) were recruited and in-depth interviews were conducted with each participant using semi-structured and open-ended questions. Thematic analysis, guided by the Voice-Centred Relational (VCR) method, was employed to analyze the data. Five major themes were identified, namely, life as a family caregiver, Chinese cultural understandings of disease and death, interdependency in the Chinese family, experiences with Canadian health care services, and future considerations. Understanding the lived experiences of Chinese family caregivers is an important first step in making healthcare providers aware of relevant aspects of Chinese culture so they may better provide palliative and end-of-life care to Chinese immigrants and their families in the future.
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 | 0.010 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".