Distress and quality of life : An exploratory study of Chinese-speaking cancer patients and family caregivers in Canada
Bibliographic record
Abstract
Objective: This study explores the relationships of patient distress, family caregiver distress and patient quality of life (QOL) in a Chinese-speaking cancer population, using a comparison group of Anglophone patients and family caregivers in British Columbia, Canada. Methods: Quantitative regression analysis of survey data was conducted to examine the direct and indirect effects of patient and family caregiver distress on patient QOL based on data from 29 Chinese-speaking and 28 Anglophone dyads. Semi-structured interviews were conducted with a purposive sample of ten Chinese-speaking patients and six family caregivers to further clarify the interrelationships among patient distress, family caregiver distress and patient QOL. Results: Patient distress was a significant predictor of patient QOL (β = -.79). The effects of patient age on patient emotional well-being were mediated by patient distress, such that lower distress in older patients explained better emotional functioning. A key theme from the qualitative data analysis was the emotional regulation of patient and family caregiver, where both sought to regulate their emotions to protect each other from further cancer-related distress. Conclusion: These results highlight the importance of understanding the patients’ and family members’ cultural and social context, in patient- and family-centred care.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".