Medical Tourism for Cancer Treatment: Trends, Trajectories, and Perspectives From African Countries
Bibliographic record
Abstract
PURPOSE: Cancer continues to be a significant public health concern. Sub-Saharan Africa (SSA) struggles with a lack of proper infrastructure and adequate cancer care workforce. This has led to some countries relying on referrals of cancer care to countries with higher income levels. In some instances, patients refer themselves. Some countries have made it their goal to attract patients from other countries, a term that has been referred to as medical tourism. In this article, we explore the current status of oncology-related medical tourism in SSA. METHODS: This was a cross-sectional study. The study participants included oncologists, surgeons, and any other physicians who take care of patients with cancer. A predesigned questionnaire was distributed through African Organization for Research and Training in Cancer member mailing list and through study team personal contacts and social media. RESULTS: A total of 52 participants from 17 African countries with a 1.6:2 male to female ratio responded to the survey. Most (55.8%) of the respondents were from Eastern African countries. The majority (92%) of study participants reported that they knew patients who referred themselves abroad, whereas 75% referred patients abroad, and the most common (94%) referral destination was India. The most common (93%) reason for referral was perception of a higher quality of care in foreign health institutions. CONCLUSION: The findings suggest the need to improve local health care systems including building trust of the system among general population. The study highlights potential financial toxicity, and it adds to the current emphasis on return of investment on homegrown workforce and cancer treatment infrastructure.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".