Quality of life of family caregivers of cancer patients in Singapore and globally
Bibliographic record
Abstract
INTRODUCTION: Family caregivers of cancer patients often suffer from impaired quality of life (QOL) due to stress arising from the responsibility of caregiving. Most research on such QOL impairments was conducted in Western populations. Thus, this exploratory study sought to (a) examine the QOL levels of family caregivers of cancer patients in an Asian population in Singapore, in relation to caregivers from other countries within and outside of Asia; and (b) investigate the association between sociodemographic factors and QOL impairments in family caregivers in Singapore. METHODS: A total of 258 family caregivers of cancer patients who were receiving outpatient treatment completed the Caregiver Quality of Life Index-Cancer (CQOLC) and a sociodemographic survey. We compared the published CQOLC total scores from Turkey, Iran, Taiwan, South Korea, the United Kingdom, the United States and Canada with the Singapore dataset and examined the demographic relationships. RESULTS: Caregivers in Singapore and Asia had lower CQOLC total scores than their Western counterparts. Caregivers who were male, of Chinese ethnicity, had parental relationships with their care recipient, or cared for advanced-stage cancer patients were found to have impaired QOL. CONCLUSION: The findings of this study highlight possible areas in which support can be provided for family caregivers of cancer patients, and underscore the need to reconcile cultural diversity, values, societal expectations and demographic characteristics in Singapore.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".