Determinants of Access to Essential Surgery in the Democratic Republic of Congo
Bibliographic record
Abstract
INTRODUCTION: In the Democratic Republic of Congo (DRC), the determinants and barriers of essential surgical care are not well described, hindering efforts to improve national surgical programs and access. METHODS: A cross-sectional study evaluated access to essential surgery in the Butembo and Katwa health zones in the North Kivu province of DRC. A double-clustered random sample of community members was surveyed using questions derived from the Surgeons OverSeas Surgical Needs Assessment Survey, a validated tool to determine the reasons for not seeking, reaching, or receiving a Bellwether surgery (i.e., caesarean delivery, laparotomy, and external fixation of a fracture) when needed. RESULTS: Overall, 887 households comprising 5944 community members were surveyed from April to August 2022. Six percent (n = 363/5944) of the study population involving 35% (n = 309/887) households needed a Bellwether surgery in the previous year, 30% (n = 108/363) of whom died. Of those who needed surgery, 25% (n = 78) did not go to the hospital to seek care and were more likely to find transportation unaffordable (P = 0.042). The most common reasons for not seeking care were lack of funds for hospitalization, prior poor hospital experience, and fear of hospital care. CONCLUSIONS: Access and delivery of essential surgery are drastically limited in the North Kivu province of the DRC, such that a quarter of households needing surgery fails to seek surgical care. Poor access was predominantly driven by households' inability to pay for surgery and community distrust of the hospital system.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".