Symptoms, Psychological Distress, Social Support, and Quality of Life of Chinese Patients Newly Diagnosed With Gastrointestinal Cancer
Bibliographic record
Abstract
This study aims to describe symptoms, psychological distress, social support, and quality of life of Chinese patients newly diagnosed with gastrointestinal tract (GIT) cancer, and to identify the extent to which demographic, physical, and psychosocial factors predict their quality of life. A convenience sample of 146 newly diagnosed GIT cancer patients recruited from 3 major hospitals in Shanghai completed a self-report questionnaire. The questionnaire was designed to obtain demographic and medical data and measures of symptoms, psychological distress, social support, health-related quality of life (HRQoL), and global quality of life (GQoL). Measures developed in English were translated into Chinese using the procedure advocated by WHO. The results showed that the most common signs and symptoms reported were fatigue, pain, and weight loss; 28% of the patients were depressed; and overall, patients had a moderate quality of life. Comparative analyses found some difference on measures for demographic and diagnostic subgroups. Depression, symptom distress, and social support accounted for 44% of the total variance for HRQoL, while perceived financial difficulty and symptom distress accounted for 20% of the total variance for GQoL. Findings from this research give insights into the importance of quality of life assessment, symptom management, and intervention to improve the quality of life of Chinese cancer patients. It also raises questions about measures of quality of life that are culturally relevant.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".