Abstract 58: Leaving Cancer Patients Behind for Greener Pastures: The Clinical Oncology Workforce in Nigeria
Bibliographic record
Abstract
Abstract Purpose: For a population of over 201 million, Nigeria has only 4 doctors per 10,000 patients and 16.1 nurses, midwives per 10,000 patients, and less than 100 clinical oncologists for over 100,000 cancer patients. While Nigeria has one of the worst disease burdens in the world and a workforce shortage; 9 in 10 Nigerian physicians are seeking opportunities to leave for the USA, UK, and Canada. To improve oncology care in Nigeria, it may be important to understand the push and pull factors contributing to the migration of the clinical oncology (CO) workforce. Mathew (2018), Vanderpuye, et al., (2019), Balogun, et al., (2017), and Adebayo, (2016) have done research on CO; however, their studies were vastly focused on the African continent and not country-specific nor focused on the CO workforce in Nigeria.The aim of this study is to explore the push and pull factors to stay or leave the clinical oncology workforce in Nigeria. Methods: Using a mixed-method research approach, 80 participants completed the questionnaire and 9 participants responded to semi-structured interviews. Multiple linear regression and Grounded theory were used for the data analysis. Results: The results show that CO workload and satisfaction were significantly related to turnover intention. The qualitative results showed that CO as a new area of specialization, mentorship, career growth, and attractiveness of radiation science are the pull factors. While, high CO workload, poor healthcare system, poor remuneration, corruption in the public sector, and a few other themes are push factors. Empathy for patients, patriotism and a sense of fulfillment unexpectedly emerged as retention factors in the study. Conclusion: Nigeria can improve patient treatment outcomes by the reduction of CO workload through the employment of more CO. More CO can be available for employment when they are attracted, and their training is optimized. Those employed can be retained by improving working conditions and introducing work benefits. Our recommendations are that health leaders should create more CO training and awareness of CO in Nigeria. Citation Format: Runcie C.W. Chidebe, Tochukwu C. Orjikor, Onyinye Balogun, Adedayo Joseph, Samantha Toland, Alison Simons. Leaving Cancer Patients Behind for Greener Pastures: The Clinical Oncology Workforce in Nigeria [abstract]. In: Proceedings of the 11th Annual Symposium on Global Cancer Research; Closing the Research-to-Implementation Gap; 2023 Apr 4-6. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(6_Suppl):Abstract nr 58.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".