One-year outcomes of traumatic injuries among survivors in Ethiopia: a cross-sectional study on the employment outcomes and functioning state
Bibliographic record
Abstract
Background: Traumatic injury is one of the top public health challenges globally. Injury survivors often experience poor health and functioning and restricted participation in employment. In Ethiopia, there is a paucity of evidence about the long-term consequences of injuries, particularly about their employment outcomes and disability status. This study characterizes injury survivors by their preinjury status, injury characteristics, postinjury employment outcomes and disability status 1 year post injury. Methods: An institution-based cross-sectional study was conducted on injury survivors who received services from a large public hospital in Addis Ababa. Medical records of all emergency room patients who visited the hospital within a 3-month period were reviewed to identify those who were eligible. A structured questionnaire was completed using a telephone interview. Descriptive statistics were used to characterize the outcomes. Results: Of the 254 participants, 78% were men, 48% were young adults (age 25-39 years), 41% were injured by road traffic collision, 52% were admitted to the hospital for up to a week and only 16% received compensation for the injury. Before the injury, 87% were working in manual labor. One-year after the injury, the total return to work (RTW) rate was 59%; 61% of participants experienced some level of disability, 33% had at least one type of chronic illness and 56% reported challenges of physical stressors when attempting to RTW. Among the 150 who returned to work, 46% returned within 12 weeks, 78% to the same employer and most received support from multiple sources, including community-level institutions (88%) and families/friends (67%). Conclusion: Traumatic injury substantially impacted the employment outcomes of survivors and contributed to increased disability in Ethiopia. This study lays a foundation for future research and contributes crucial evidence for advocacy to improve injury prevention and trauma rehabilitation in low and middle-income contexts. Level of evidence: II.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".