Does palliative care improve the quality of life for cancer patients in three tertiary hospitals in South Africa?
Bibliographic record
Abstract
BACKGROUND: Quality of life (QoL) is a crucial treatment outcome for cancer patients, who often experience significant symptom burden and distress. Despite its benefits, access to palliative care remains limited in many settings, including South Africa. This study assessed changes in QoL before and after palliative care interventions and examined factors associated with QoL improvements. METHODS: A retrospective cohort study was conducted among cancer patients referred for palliative care at three tertiary hospitals in Johannesburg, South Africa. Interdisciplinary teams provided palliative care, and patient data on sociodemographic and clinical characteristics were collected. QoL was measured using the World Health Organization Quality Of Life-BREF (WHOQOL BREF) tool at baseline and follow-up. Paired t-tests compared mean QoL scores, and mixed regression analysis identified factors associated with QoL improvements. Data were analyzed using Stata version 18 SE. RESULTS: Among 724 patients (68.51% female, mean age 51.78 years, SD: 16.62), the most common cancers were breast (29.83%), gastrointestinal/hepatobiliary (21.27%), and cervical (16.71%). Baseline QoL mean (SD) scores were low across all domains. Following palliative care, significant improvements (p < 0.001) were observed: general health improved from 43.64 (26.17) to 66.28 (26.71); physical health from 48.46 (15.45) to 53.42 (15.08); psychological health from 57.54 (17.39) to 65.44 (18.75); environmental health from 60.97 (19.90) to 71.49 (18.84) and social health from 59.26 (25.60) to 71.08 (23.54) (all p < 0.001). HIV-negative status was associated with better outcomes across all domains, with coefficients ranging from 0.19 to 0.46 (p < 0.05) compared to those living with HIV. CONCLUSIONS: Palliative care significantly improved QoL across all measured domains among cancer patients at tertiary hospitals in Johannesburg. These findings highlight the need to integrate palliative care into routine oncology treatment to enhance patient well-being.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".