Psychosocial Distress and the Quality of Life of Cancer Patients in Rural Hospitals in Limpopo Province: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: The diagnosis and treatment of cancer are associated with substantial physical, psychological, and social morbidity for most patients. Distress can be seen as an unpleasant experience of an emotional, psychological, social, or spiritual nature that interferes with the ability to cope with cancer treatment. PURPOSE: The aim was to understand patients' experiences of distress in their context and to analyze and interpret the findings. METHOD: An explorative, descriptive qualitative study was conducted among cancer patients receiving treatment and care at rural hospitals in Limpopo. A face-to-face individual interview was conducted to determine the participants' cancer-related experiences and quality of life. Thematic analysis was conducted following Tesch's method, and the themes developed were subjected to a triangulation process to ensure the validity and rigor of the findings. FINDINGS: The participants revealed experiences of symptomatic distress resulting in biopsychosocial distress such as pain, fatigue, emotional distress related to prognosis and uncertainty about the future, psychosocial distress related to a lack or absence of support, financial instability, and poor self-esteem. CONCLUSIONS: Cancer patients face many challenges during their treatment journey. Participants were drained by anxiety and uncertainty of the cancer trajectory and required psychosocial support. The oncology team must provide supportive preventive measures for side effects management and culture-sensitive psychotherapy at an early stage to improve their quality of life.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".