Challenges of Surgery in Developing Countries: A Survey of Surgical and Anesthesia Capacity in Uganda’s Public Hospitals
Bibliographic record
Abstract
BACKGROUND: There are large disparities in access to surgical services due to a multitude of factors, including insufficient health human resources, infrastructure, medicines, equipment, financing, logistics, and information reporting. This study aimed to assess these important factors in Uganda's government hospitals as part of a larger study examining surgical and anesthesia capacity in low-income countries in Africa. METHODS: A standardized survey tool was administered via interviews with Ministry of Health officials and key health practitioners at 14 public government hospitals throughout the country. Descriptive statistics were used to analyze the data. RESULTS: There were a total of 107 general surgeons, 97 specialty surgeons, 124 obstetricians/gynecologists (OB/GYNs), and 17 anesthesiologists in Uganda, for a rate of one surgeon per 100,000 people. There was 0.2 major operating theater per 100,000 people. Altogether, 53% of all operations were general surgery cases, and 44% were OB/GYN cases. In all, 73% of all operations were performed on an emergency basis. All hospitals reported unreliable supplies of water and electricity. Essential equipment was missing across all hospitals, with no pulse oximeters found at any facilities. A uniform reporting mechanism for outcomes did not exist. CONCLUSIONS: There is a lack of vital human resources and infrastructure to provide adequate, safe surgery at many of the government hospitals in Uganda. A large number of surgical procedures are undertaken despite these austere conditions. Many areas that need policy development and international collaboration are evident. Surgical services need to become a greater priority in health care provision in Uganda as they could promise a significant reduction in morbidity and mortality.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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 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".