Injury, Disability and Access to Care in Rwanda: Results of a Nationwide Cross‐Sectional Population Study
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it