Brain Drain in Cancer Care: The Shrinking Clinical Oncology Workforce in Nigeria
Bibliographic record
Abstract
PURPOSE: A recent estimate indicates that Nigeria has about 70 clinical oncologists (COs) providing care for 124,815 patients with cancer and its 213 million total population. This staggering deficit is likely to worsen as about 90% of Nigerian physicians are eager to leave the country for perceived greener pastures in the United States, the United Kingdom, Canada, etc. Previous studies have examined general physician migration abroad; however, the CO workforce in Nigeria has been barely considered in the workforce literature. This study examined the push and pull factors to stay or leave the CO workforce and Nigeria. METHODS: Using a correlational design, 64 COs completed turnover intention (TI), workload, and satisfaction measures. Multiple linear regression was used for the data analysis. RESULTS: < .05) were significantly related to TI. The number of outpatients seen was also positively linked to TI. Hence, the more outpatients a CO sees, the higher the intention to leave. The United States (31%), the United Kingdom (30%), and Canada (10%) were the top countries of destinations for Nigerian COs. CONCLUSION: Higher CO workload is a push factor propelling the intention to leave CO practice and relocate to other countries. Nigeria's new National Cancer Control Plan and the Federal Ministry of Health need to explore innovative approaches to attract and retain the CO workforce, which would lead to improvement in cancer survival and outcomes. Increasing the number of CO programs and positions available, improving work conditions, and introducing work benefits may mitigate the shrinking CO workforce in Nigeria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".