Rwandan Surgical and Anesthesia Infrastructure: A Survey of District Hospitals
Bibliographic record
Abstract
BACKGROUND: In low-income countries, unmet surgical needs lead to a high incidence of death. Information on the incidence and safety of current surgical care in low-income countries is limited by the paucity of data in the literature. The aim of this survey was to assess the surgical and anesthesia infrastructure in Rwanda as part of a larger study examining surgical and anesthesia capacity in low-income African countries. METHODS: A comprehensive survey tool was developed to assess the physical infrastructure of operative facilities, education and training for surgical and anesthesia providers, and equipment and medications at district-level hospitals in sub-Saharan Africa. The survey was administered at 21 district hospitals in Rwanda using convenience sampling. RESULTS: There are only nine Rwandan anesthesiologists and 17 Rwandan surgeons providing surgical care for a population of more than 10 million. The specialty-trained Rwandan surgeons and anesthesiologists are practicing almost exclusively at referral hospitals, leaving surgical care at district hospitals to the general practice physicians and nurses. All of the district hospitals reported some lack of surgical infrastructure including limited access to oxygen, anesthesia equipment and medications, monitoring equipment, and trained personnel. CONCLUSIONS: This survey provides strong evidence of the need for continued development of emergency and essential surgical services at district hospitals in Rwanda to improve health care and to comply with World Health Organization recommendations. It has identified serious deficiencies in both financial and human resources-areas where the international community can play a role.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".