Medical oncology in India: Workload, infrastructure, and delivery of care
Bibliographic record
Abstract
Abstract Background: The growing burden of cancer within India has implications across the health system including operational delivery of cancer care and planning for human health resources. Here, we report the Indian results of a global survey of medical oncology (MO) workload in comparison to medical oncologists (MOs) in other low-middle- income countries (LMICs). Methods: An online survey was distributed through a snowball method through national oncology societies to chemotherapy-prescribing physicians in 22 LMICs. The survey was distributed to Indian MOs by the Indian Society of Medical and Pediatric Oncology and the National Cancer Grid of India. The workload was measured as the annual number of new cancer patient consults seen per oncologist. Results: One hundred and forty-seven oncologists from LMICs completed the survey; 82 from India and 65 from other LMICs. About 59% (48/82) of Indian MOs reported working exclusively in the private health system compared to 23% (15/65) of MOs in other LMICs (P < 0.001). The median number of annual consults per MO was 475 in India compared with 350 in other LMICs. The proportion of MOs seeing >1000 new consults/year was 24% (20/82) in India and 20% (13/65) in other LMICs (P = 0.530). The median number of patients seen in a full-day clinic was 35 in India and 25 in other LMCs (P = 0.003); 26% of Indian MO reported seeing >50 patients per day. Compared to other LMICs, Indian MOs worked more days/week (median 6 vs. 5, P < 0.001) and hours/week (median 51–60 vs. 41–50, P = 0.004) and had less annual leave for vacation (3 weeks vs. 4, P = 0.017). Conclusion: Indian MOs have higher clinical volumes and workload than MOs in other LMICs and substantially higher workload than MOs in high-income countries. Indian health policymakers should consider alternative models of care and increasing MO workforce supply to address the growing burden of cancer.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".