Quality of Life among Ethiopian Cancer Patients: A Systematic Review of Literatures
Bibliographic record
Abstract
Background: Assessment of quality of life (QoL) in cancer patients can provide an overall picture of the patient's adaptation to the disease and maintain long-term health and well-being. Determining the QoL in cancer patients could help with better care and could be as prognostic as medical factors and the survival benefit that pharmacological treatment could provide. The main objective of this review was to determine the prevalence of QoL among Ethiopian adult cancer patients. Methods: A systematic review was conducted using the "Preferred Reporting Results of Systematic Reviews and Meta-Analyses" guidelines. Databases such as PubMed/Medline, CINAHL, AMED, Embase, the Cochrane Library, ScienceDirect, World Health Organization's Hinari portal, and African Journals Online databases were searched from January 2022 to June 2022. Google, Google Scholar, and university repositories were used to access unpublished studies. Two reviewers independently screened the data using a standardized data extraction format and appraised their quality using the Newcastle-Ottawa Scale. Results: In this review, 10 studies were included. The prevalence of poor QoL ranged from 52 to 89.6. The physical, role, social, and emotional functioning were the most affected domains of QoL and have been significantly associated with QoL. Financial difficulty was the most important predictor of QoL among Ethiopian cancer patients. Pain, dyspnea, nausea, vomiting, and poor appetite were also reported as predictors of QoL. Low income, lower educational status, unmarried status, underweight, anxiety, and depression, advanced stage of cancer, patients with ≤2 cycles of chemotherapy, and the presence of comorbid diseases were significantly correlated with QoL. Conclusions: The QoL of an Ethiopian cancer patient was low. Quality of life assessments, appropriate symptom management, integration of psycho-oncology care, and providing economic support shall be considered to improve their QoL.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".