Not a “first world problem”—Care of the anesthetist in East and Southern Africa
Bibliographic record
Abstract
Burnout and related concepts such as resilience, wellness, and taking care of healthcare professionals have become increasingly prevalent in the medical literature. Most of the work in this area comes from high-income countries, with the remainder from upper-middle-income countries, and very little from lower-middle-income or low-income countries. Sub-Saharan Africa is particularly poorly represented in this body of literature. Anglo-American concepts are often applied to different jurisdictions without consideration of cultural and societal differences. However, anesthesia providers in this region have unique challenges, with both the highest burden of "surgical" disease in the world and the least resources, both in terms of human resources for health and in terms of essential drugs and equipment. The effect of burnout on healthcare systems is also likely to be very different with the current human resources for the health crisis in East and Central Africa. According to the Joint Learning Initiative Managing for Performance framework, the three essential factors for building a workforce to effectively support a healthcare system are coverage, competence, and motivation. Current efforts to build capacity in anesthesia in East and Southern Africa focus largely on coverage and competence, but neglect motivation at the risk of failing to support a sustainable workforce. In this paper, we include a review of the relevant literature, as well as draw from personal experience living and working in East and Southern Africa, to describe the unique issues surrounding burnout, resilience, and wellness in this region.
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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 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".