Epidemiology of unintentional child injuries in the South-East Asia Region: a systematic review
Bibliographic record
Abstract
All the 11 members of the South-East Asia Region (SEAR) of the World Health Organization are categorised as low- and middle-income countries. This region has over a quarter of the world's total population but comprises about one-third of the world's unintentional injury-related deaths. There is a paucity of good-quality mortality and morbidity data from most of these countries. This is the first systematic review of community-based surveys on child injuries that summarises evidence from child injury studies from the SEAR countries. The included papers reported varying estimates of overall non-fatal unintentional injury rates across the countries, from 15/1000 children in Thailand to as high as 342/1000 children in India. The fatal injury rates were also found to be varying. This review revealed a need for strengthening child injury research using standard methodologies across the region and for promoting the dissemination of the results.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| 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 | 0.001 |
| 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".