Clinical Oncology Workload in Sri Lanka: Infrastructure, Supports, and Delivery of Clinical Care
Bibliographic record
Abstract
PURPOSE: Sri Lanka is a lower middle-income country undergoing a demographic transition with an increasing aging population. This has given rise to a higher burden of noncommunicable diseases including cancer. A well-trained oncology workforce is essential to address this growing public health challenge. Understanding the baseline status of the clinical oncology workforce is an essential step to improving cancer care delivery in Sri Lanka. METHODS: In this cross-sectional study, we distributed a web-based survey to all clinical oncologists in Sri Lanka. The survey captured data regarding clinical workload, demographic details, practice setting, and perceived barriers to quality patient care. RESULTS: A total of 41 of 54 oncologists responded to the survey, and all participants had training in clinical oncology. Thirty-seven (90%) of 41 oncologists treated both solid and hematologic malignancies, and the median duration of independent practice was 5 years. Almost two thirds of the oncologists (26 of 41, 63%) work at an academic center, and two thirds of the oncologists (27 of 41, 66%) work in both public and private sectors. A majority of the oncologists (26 of 41, 63%) were on-call 7 days per week. More than half of the oncologists saw over 400 new patient consults per year. With regard to barriers to quality patient care, most of the concerns relate to the scarcity of resources. CONCLUSION: This study sheds significant light about the clinical oncology workload landscape in Sri Lanka. Compared with other low- and middle-income countries, Sri Lankan clinical oncologists are faced with a very high workload, which may affect delivery or care.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".