Health-related quality of life and its socio-economic and cultural predictors among advanced cancer patients: evidence from the APPROACH cross-sectional survey in Hyderabad-India
Bibliographic record
Abstract
BACKGROUND: Patients with advanced cancer often experience poor health-related quality-of-life (HRQoL) due to cancer and treatment-related side-effects. With India's palliative care landscape in its infancy, there is a concern that advanced cancer patients, especially individuals who are from disadvantaged populations experience poor HRQoL outcomes. We aim to assess HRQoL of advanced cancer patients in terms of general well-being (physical, functional, emotional, and social/family well-being), pain experiences, psychological state, and spiritual well-being, and determine the relationship between belonging to a disadvantaged group and HRQoL outcomes. We hypothesize that patients from disadvantaged or minority backgrounds, identified in this paper as financially distressed, female, lower years of education, lower social/family support, minority religions, and Non-General Castes, would be associated with worse HRQoL outcomes compared to those who are not from a disadvantaged group. METHODS: We administered a cross-sectional survey to 210 advanced cancer patients in a regional cancer center in India. The questionnaire included standardized instruments for general well-being (FACT-G), pain experiences (BPI), psychological state (HADS), spiritual well-being (FACT-SP); socio-economic and demographic characteristics. RESULTS: Participants reported significantly lower general well-being (mean ± SD) (FACT-G = 62.4 ± 10.0) and spiritual well-being (FACT-SP = 32.7 ± 5.5) compared to a reference population of cancer patients in the U.S. Patients reported mild to moderate pain severity (3.2 ± 1.8) and interference (4.0 ± 1.6), normal anxiety (5.6 ± 3.1) and borderline depressive symptoms (9.7 ± 3.3). Higher financial difficulty scores predicted most of the HRQoL domains (p ≤ 0.01), and being from a minority religion predicted lower physical well-being (p ≤ 0.05) and higher pain severity (p ≤ 0.05). Married women reported lower social/family well-being (p ≤ 0.05). Pain severity and interference were significant predictors of most HRQoL domains. CONCLUSIONS: Advanced cancer patients, especially those with lower financial well-being and belonging to minority religions, reported low physical, functional, emotional, social/family, and spiritual well-being, and borderline depressive symptoms. Future studies should be directed at developing effective interventions supporting vulnerable groups such as those with financial distress, and those belonging to minority religions.
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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 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".