Navigating cancer care in Cameroon: a theory-guided inquiry on patient experiences at Mbingo Baptist Hospital
Bibliographic record
Abstract
BACKGROUND: Cancer remains a leading cause of morbidity and mortality globally, with rising incidence rates, especially in low- and middle-income countries (LMICs). This burden is pronounced in Sub-Saharan Africa (SSA), where Cameroon faces escalating cancer challenges, primarily due to inadequate healthcare infrastructure and limited access to early detection and treatment. The study aimed to explore the experiences of cancer patients at Mbingo Baptist Hospital in Cameroon in Cameroon, focusing on the barriers to obtaining quality diagnosis, treatment, and follow-up care, and to examine the impact of these challenges on their physical, emotional, and social well-being. METHODS: This study employed a qualitative descriptive design, conducting in-depth interviews with eleven cancer patients in December 2023 and January 2024. Participants were selected using purposive sampling, and data were analyzed using thematic analysis to identify key barriers in the cancer care pathway. The biopsychosocial model guided the exploration of patients' experiences, capturing the interplay between biological, psychological, and social dimensions of their healthcare journey. RESULTS: The analysis revealed significant delays in diagnosis, substantial financial burdens, and emotional and psychological distress among patients. Key themes identified include challenges in the diagnosis and treatment processes, the financial impact of cancer care, emotional and psychosocial repercussions, and difficulties in accessing healthcare services. Despite facing these obstacles, patients also reported instances of resilience and support within their families and communities. CONCLUSION: The study underscores the urgent need for systemic improvements in cancer care in Cameroon and similar contexts. Enhancing healthcare infrastructure, broadening financial protection, and fostering awareness and early detection are imperative. Additionally, integrating a holistic care approach that considers the biopsychosocial aspects of patient health is crucial for improving outcomes. Addressing these recommendations requires collaborative efforts from governmental and non-governmental organizations, healthcare providers, and the international community to tailor cancer control strategies to the unique needs of LMICs, aiming to alleviate the cancer burden and enhance patient 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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".