Challenges and Recommendations for Improving Cancer Research and Practice in Nigeria: <i>A Qualitative Study With Multi-Stakeholders in Oncology Research and Practice</i>
Bibliographic record
Abstract
BACKGROUND: Cancers, with increasing incidence and mortality rates, constitute a leading public health problem in Nigeria. As the burden of cancer in Nigeria increases, research and quality service delivery remain critical strategies for improved cancer control across the continuum of care. This study contextualizes the challenges and gaps in oncology research and practice in Nigeria, and presents recommendations to address the gaps. METHODS: This qualitative study was conducted among interprofessional and interdisciplinary stakeholders in oncology healthcare practice and research in academic settings, between July and September 2021. Key-informant interviews were held with six stakeholders and leaders in nursing, pharmacy, and medicine across the six geopolitical zones of Nigeria, and twenty-four in-depth interviews with early- or mid-career researchers or healthcare professionals involved in cancer prevention and treatment were conducted. The data were analyzed using a deductive thematic analysis approach and coded using the NVIVO 12 software. RESULTS: Five sub-themes were identified as major challenges to oncology research, including poor funding, excessive workload, interprofessional rivalry, weak collaboration, and denial of cancer diagnosis by patients. Challenges identified for oncology practice were poor governance and financing, high costs of oncology treatments, poor public awareness of cancer, workforce shortage, and interprofessional conflicts. Recommended strategies for addressing these challenges were improved financing of oncology research and practice by government and relevant stakeholders, increasing interest of medical, nursing, and pharmaceutical students in oncology research through curricula-based approach and mentorship, increased oncology workforce, and improved intra- and inter-professional collaboration. CONCLUSION: These data highlight the challenges and barriers in oncology practice and research in Nigeria, and underscore the urgent need for increased investments in infrastructure to provide interdisciplinary and interprofessional research training for high-quality care. Only then can Nigeria effectively tackle the current and impending cancer burden in the country.
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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 | 0.046 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 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".