Injuries among adolescents in the United Arab Emirates
Bibliographic record
Abstract
This study examines the profile of injuries among adolescents in the United Arab Emirates (UAE) and identifies related factors associated with injury. A cross-sectional study design determined incidence of injury for a sample of 6363 adolescents. Data collected information on injury in the past 12 months, socio-demographic, behavioural and sensory data. Logistic regression modelling was used to examine predictors of physical injury for the past 12 months. Among participants, 18% experienced injury; the three top causes include accidental falls (38%), being struck by an object or person (18%), and motor vehicle injuries (MVI) (13%). The majority of injuries took place at the participant's home and surrounding area. Identified risk factors that are amenable for prevention include smoking behaviour, exposure to smoking, physical activity profile, family income, and speeding behaviour. Our findings highlight the need for public health policies and education programmes that reduce injury among the UAE adolescent population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".