History of injury in a developing country: a scoping review of injury literature in Lebanon
Bibliographic record
Abstract
BACKGROUND: Lebanon, an Eastern Mediterranean country, suffers a large burden of injury as a consequence of conflict and war, political instability, and the lack of policies and safety regulations. This article aims to systematically map and comprehensively describe the injury research literature in Lebanon and, to identify gaps for future research. METHODS: MEDLINE, Embase, Eric and SafetyLit, and the grey literature, including conference proceedings, theses and dissertations, government and media reports, were searched without any date or language limits. Data were extracted from 467 documents using REDCap. RESULTS: War-related injuries were the most prevalent type of injury in Lebanon, followed by homicide and other forms of violence. While existing literature targeted vulnerable and at-risk populations, the vast majority focused solely on reporting the prevalence of injuries and associated risk factors. There are considerable gaps in the literature dealing with the integration of preventive programs and interventions across all populations. CONCLUSIONS: Lebanon, historically and currently, experiences a high number of injuries from many different external causes. To date, efforts have focused on reporting the prevalence of injuries and making recommendations, rather than implementing and evaluating interventions and programs to inform policies. Future injury related work should prioritize interventions and prevention programs.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".