QUESTIONING THE UTILITY OF ‘CULTURAL COMPETENCY’ IN CARING FOR OLDER CHINESE PATIENTS AT THE END-OF-LIFE
Bibliographic record
Abstract
With increasing cultural diversity in North America, ensuring that healthcare professionals are able to provide appropriate care to individuals from a wide range of cultural backgrounds, commonly described as “cultural competency”, has become a priority issue within the healthcare system. More specifically, culturally competent end-of-life care is essential for clinicians to prioritize as the individualized needs of older patients and their families, regardless of cultural background or ethnicity, are unique and context driven. The aim of this study was to explore the usefulness of “cultural competency” as a way to improve the ability of healthcare practitioners to meet the healthcare needs of elderly Chinese patients at the end-of-life in Canada. Using a qualitative research design, 49 in-depth interviews with 23 participants were conducted. Our results are organized into six vignettes, which illustrate how “Chinese culture” is not a commonly understood or experienced phenomenon, but that the individual ways in which “culture” is interpreted by patients and healthcare providers around issues of death and dying. Furthermore, the results from this study challenge the assumption that all members of an ethnic or cultural group share similar views on end-of-life and palliative care. Finally, how and why cultural explanations are used to justify health care-related requests and decisions, at the end-of life are explored. Not all “Chinese patients” had the same “cultural” needs or sensitivities and underscore the need for good communication with patients and their family members to facilitate healthcare providers’ understanding of patients’ needs and values regardless of their ethnic background.
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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 | 0.042 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".