Injury, Disability and Access to Care in Rwanda: Results of a Nationwide Cross‐Sectional Population Study
Bibliographic record
Abstract
BACKGROUND: Disparities in access to quality injury care are a growing concern worldwide, with over 90 % of global injury-related morbidity and mortality occurring in low-income countries. We describe the use of a survey tool that evaluates the prevalence of surgical conditions at the population level, with a focus on the burden of traumatic injuries, subsequent disabilities, and barriers to injury care in Rwanda. METHODS: The Surgeons OverSeas Assessment of Surgical Need (SOSAS) tool is a cross-sectional, cluster-based population survey designed to measure conditions that may necessitate surgical consultation or intervention. Questions are structured anatomically and designed around a representative spectrum of surgical conditions. Households in Rwanda were sampled using two-stage cluster sampling, and interviews were conducted over a one-month period in 52 villages nationwide, with representation of all 30 administrative districts. Injury-related results were descriptively analyzed and population-weighted by age and gender. RESULTS: A total of 1,627 households (3,175 individuals) were sampled; 1,185 lifetime injury-related surgical conditions were reported, with 38 % resulting in some form of perceived disability. Of the population, 27.4 % had ever had a serious injury-related condition, with 2.8 % having an injury-related condition at the time of interview. Over 30 % of household deaths in the previous year may have been surgically treatable, but only 4 % were injury-related. CONCLUSIONS: Determining accurate injury and disability burden is crucial to health system planning in low-income countries. SOSAS is a useful survey for determining injury epidemiology at the community level, which can in turn help to plan prevention efforts and optimize provision of care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".