Cross-cultural validation of the McGill Quality of Life questionnaire in Hong Kong Chinese
Bibliographic record
Abstract
The main focus of palliative care services is to improve patients' quality of life (QOL). The potential value of assessment of QOL in palliative care is being increasingly recognized. The McGill Quality of Life questionnaire (MQOL) is designed specifically for palliative care patients, but its cross-cultural validity needs to be determined before it can be applied in populations of different cultures and ethnic groups. The cross-cultural validity of MQOL was investigated using a translated and modified version in Chinese--the MQOL-HK--in 462 palliative care patients in Hong Kong. Results show that the MQOL-HK is acceptable, valid and reliable. There is good acceptability, construct validity, convergent and divergent validity, test-retest and inter-rater reliability. Our study confirms that QOL does have cross-culturally robust constructs. Principal components analysis shows that the domains of physical, psychological, existential and support are all relevant and applicable in Chinese culture. Multiple regression analysis reveals that existential domain is the most important domain in predicting overall QOL. 'Face', eating and sex are additional facets of QOL that also need to be considered. The worst physical symptom on admission is the item of QOL with the lowest score, which need more care and attention by palliative care workers. A cross-culturally validated QOL instrument cannot just help ensure an accurate evaluation of profile, determinants, and changes of QOL, but is also a valuable asset for future comparison and evaluation of palliative care services and interventions across the world.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".