Intentional injuries among Ugandan youth: a trauma registry analysis
Bibliographic record
Abstract
PURPOSE: To determine intentional injury burden, incident characteristics, and outcomes among Ugandan youth. METHODS: A cross sectional analysis of trauma registry data from accident and emergency units of five regional referral hospitals was conducted. Data had been prospectively collected from all patients accessing injury care at the five sites between July 2004 and June 2005: youth records were analysed. RESULTS: Intentional injuries among youth victims, especially school-age males, are common in all five regions, constituting 7.3% of their injury burden with a male dominance. Intentional youth victimisation mainly occurred at home, on roads, and in public places; incidents were largely due to blunt force, stabs/cuts, and gunshots in general, although variations in causes were evident depending on age. Intentional injuries among the youth victims often manifested as head, neck, and face injuries: 2% were severe and there were 4%case fatalities at 2 weeks. CONCLUSIONS AND RECOMMENDATIONS: Intentional injuries among youth victims, especially school-age males, are important contributors of injury burden in all five sites. Homes, roads, and public places are unsafe for Ugandan youth. Although guns were used in all five sites, less lethal mechanisms (blunt force, stabs/cuts, and burns) are the most common with variations between locations. Incidents involving teenage housewives could reflect underlying problem of domestic violence. Community based studies could be highly informative. Youth should be prioritised for prevention of injuries both in and out of school.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".