Determinants of quality of life among patients with breast cancer in Africa: a systematic review
Bibliographic record
Abstract
AIM: To detect and describe determinants influencing the quality of life among patients with breast cancer across Africa. METHODS: Applying the PRISMA methodology, we searched the PubMed, Scopus, and Web of Science databases from inception through January 2024 using the following search terms: breast cancer, quality of life, and Africa. The studies selected aimed to identify the factors that impact the quality of life of African women with breast cancer. The methodological rigour of each publication was assessed using the Newcastle-Ottawa Scale, which was adjusted for both cohort and cross-sectional study designs. RESULTS: 22 studies were included in this systematic review, consisting of 15 (68%) cross-sectional studies and 7 (32%) prospective studies. These studies investigated more than 34 different determinants influencing the quality of life among breast cancer. Comorbidities, chemotherapy, anxiety, and depression generally reported poorer quality of life initially, though it often showed improvement over time. Research findings varied regarding how age, marital status, income, and treatment types influence the quality-of-life outcomes among patients with breast cancer in Africa. CONCLUSION: Breast cancer patients frequently experience a worse quality of life, particularly if they are receiving chemotherapy and have additional medical conditions. This situation highlights the importance of offering patients additional therapies to improve their overall quality of life, together with comprehensive psychological and social support.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.012 | 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) | 0.002 | 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".