Injury patterns in rural and urban Uganda
Bibliographic record
Abstract
OBJECTIVES: To describe and contrast injury patterns in rural and urban Uganda. SETTINGS: One rural and one urban community in Uganda. METHODS: Community health workers interviewed adult respondents in households selected by multistage sampling, using a standardized questionnaire. RESULTS: In the rural setting, 1,673 households, with 7,427 persons, were surveyed. Injuries had an annual mortality rate of 92/100,000 persons, and disabilities a prevalence proportion of 0.7%. In the urban setting 2,322 households, with 10,982 people, were surveyed. Injuries had an annual mortality rate of 217/100,000, and injury disabilities a prevalence proportion of 2.8%. The total incidence of fatal, disabling, and recovered injuries was 116/1,000/year. Leading causes of death were drowning in the rural setting, and road traffic in the city. CONCLUSION: Injuries are a substantial burden in Uganda, with much higher rates than those in most Western countries. The urban population is at a higher risk than the rural population, and the patterns of injury differ. Interventions to control injuries should be a priority in Uganda.
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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".