Strengthening the Anesthesia Workforce in Low- and Middle-Income Countries
Bibliographic record
Abstract
The majority of the world's population lacks access to safe, timely, and affordable surgical care. Although there is a health workforce crisis across the board in the poorest countries in the world, anesthesia is disproportionally affected. This article explores some of the key issues that must be tackled to strengthen the anesthesia workforce in low- and lower-middle-income countries. First, we need to increase the overall number of safe anesthesia providers to match a huge burden of disease, particularly in the poorest countries in the world and in remote and rural areas. Through using a task-sharing model, an increase is required in both nonphysician anesthesia providers and anesthesia specialists. Second, there is a need to improve and support the competency of anesthesia providers overall. It is important to include a broad base of knowledge, skills, and attitudes required to manage complex and high-risk patients and to lead improvements in the quality of care. Third, there needs to be a concerted effort to encourage interprofessional skills and the aspects of working and learning together with colleagues in a complex surgical ecosystem. Finally, there has to be a focus on developing a workforce that is resilient to burnout and the challenges of an overwhelming clinical burden and very restricted resources. This is essential for anesthesia providers to stay healthy and effective and necessary to reduce the inevitable loss of human resources through migration and cessation of professional practice. It is vital to realize that all of these issues need to be tackled simultaneously, and none neglected, if a sustainable and scalable solution is to be achieved.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".