The Experiences of Women Living With Cervical Cancer in Africa
Bibliographic record
Abstract
BACKGROUND: Cervical cancer is the fourth most common cancer in women worldwide. However, developing countries bear 85% of the burden, with Africa sharing the highest incidence with Melanesia. OBJECTIVES: The aims of this study were to explore the experiences of women living with cervical cancer in Africa and to inform others of the extent of the work done in this field of study by synthesizing the findings of qualitative research. METHODS: The work of Sandelowski and Barroso guided the study, and 6 databases were searched to identify relevant studies using the key words Africa, cervical cancer, and experiences. RESULTS: A total of 13 studies (n = 13) met the inclusion criteria, and their findings were synthesized. The studies originated primarily from South Africa and focused on the period from diagnosis to 1 year after completing curative treatment. One overarching core theme living a life of suffering, 2 main themes, architects of suffering and mediators of suffering, and 9 subthemes were identified. CONCLUSION: Women living with cervical cancer in Africa live a life of suffering, which starts when they experience the first symptom of cervical cancer and continues well after completing treatment. The facilitators of suffering outweighed the mediators and could not be guaranteed, as it did not relieve the suffering of all. IMPLICATIONS FOR PRACTICE: Nurses practicing in Africa should be acutely aware of cervical cancer and do their utmost within their limited resources to prevent and detect the disease in its earliest stage. Religious and support interventions could be used to lessen the suffering of these women.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".