The Scale‐Up of the Global Surgical Workforce: Can Estimates be Achieved by 2030?
Bibliographic record
Abstract
BACKGROUND: The Lancet Commission on Global Surgery showed that countries with surgeon, anesthetist, and obstetrician (SAO) densities of 20-40 SAO/100,000 population were associated with improved health outcomes and recommended a global surgical workforce scale-up by 2030. Whether countries would be able to achieve such scale-up efforts in that time-frame is unknown. METHODS: A differential equation model was used to estimate the growth rate and number of SAO necessary for each country to reach the aforementioned SAO densities. Workforce data from Mexico and India were used to estimate achievable rates of SAO scale-up for middle- and low-income countries, respectively. Secular surgical growth rates were estimated to demonstrate what might occur without dedicated scale-up efforts. RESULTS: To reach at least 20 SAO/100,000 population in all countries by 2030, over 808 thousand SAO need to be trained by 2030. To reach at least 40 SAO/100,000 population, over 2.1 million SAO need to be trained. If countries adopt a scale-up rate similar to Mexico's previously achieved rate of scale-up, 66% of countries would have 20 SAO/100,000 population by 2030. If countries adopt a scale-up rate similar to India's previously achieved rate of scale-up, 56% would have 20 SAO/100,000 population by 2030. CONCLUSION: With dedicated efforts in surgical workforce scale-up, significant gains in SAO density can be made worldwide. However, without intervention, many countries are unlikely to improve their current workforce densities. Investments in workforce scale-up are likely to yield workforce gains that mirror current resource states.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".