Assessment of Health-Related Quality of Life and Distress in an Asian Community-Based Cancer Rehabilitation Program
Bibliographic record
Abstract
Cancer survivors have reduced health-related quality of life (HRQOL) and high levels of distress during and after active treatment, due to physical, psychological, and social problems. Understanding the prevalence and associations of HRQOL and distress in a patient population in the community is important when designing rehabilitation programs. This was a cross-sectional observational study conducted at a community-based cancer rehabilitation center, with the aim of investigating the prevalence and associations of HRQOL and distress in cancer patients. There were 304 patients who were recruited. We found low levels of HRQOL and high levels of distress in patients, with a mean FACT-G7 total score of 11.68, and a mean distress thermometer score of 3.51. In the multivariate regression model, significant factors for low HRQOL were metastatic disease (p = 0.025) and Malay ethnicity (p < 0.001). Regression analyses also found that significant distress was associated with family health issues (p = 0.003), depression (p = 0.001), worry (p = 0.005), breathing (p = 0.007), getting around (p = 0.012) and indigestion (p = 0.039). A high prevalence of impaired HRQOL and distress was reported in cancer survivors even in a community rehabilitation setting. The physical and psychosocial well-being of cancer survivors should be monitored and managed as part of community-based cancer rehabilitation.
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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 | 0.001 |
| 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".