Child school injury in Lebanon: A study to assess injury incidence, severity and risk factors
Bibliographic record
Abstract
BACKGROUND: School-based injuries represent a sizeable portion of child injuries. This study investigated the rates of school-based injuries in Lebanon, examining injury mechanisms, outcomes and associated risk factors. METHODS: Data were prospectively collected by intern school nurses at 11 private schools for the 2018-2019 academic year. Descriptive and inferential analyses were performed. Chi-square comparisons were conducted to determine the significance of any differences in injury rates between boys and girls for each category of school. RESULTS: 4,619 injury cases were collected. The yearly rate for school injuries was 419.1 per 1,000 children for the year 2018-2019. Boys demonstrated a significantly higher injury rate for all mechanisms of injuries, with the exception of being injured while walking, injured in the gym/sports areas, and other areas outside the playground and classroom. Elementary school children had the highest rate of injuries, nearly 2.4 times higher than kindergarten, 2.8 times higher than middle school, and 14.5 times higher than high school. Injuries to the face, upper extremities, and lower extremities were nearly 3 times more common than injuries to other areas of the body. Bumps/hits and bruises were most common-almost 3 times more likely than all other injury types. Injuries were mainly minor or moderate in severity-severe injuries were about 10 times less likely. Most injuries were unintentional, with rates nearly 5 times higher than those with unclear intent and 12 times higher than intentional injuries. CONCLUSIONS: School injuries represent a relatively common problem. Compliance with playground safety standards coupled with the implementation of injury prevention strategies and active supervision at schools can curtail child injuries and ensure a safe and injury-free school environment.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".