Factors associated with child passenger motorcycle helmet use in Cambodia
Bibliographic record
Abstract
This study examines factors associated with child passenger helmet use in five Cambodian provinces. We performed an analysis of periodic roadside observations of helmet use over a four-year period. A total of 62,039 child passengers 12 years of age and younger met inclusion criteria and were included in the analysis. Overall, 1369 (2.1%) of child passengers were observed to be wearing a helmet. Most significantly, children were six times more likely to wear a helmet if the driver was wearing a helmet (OR 6.2; 95% CI 5.1-7.5). In addition, the odds of helmet use were noted to be significantly different depending on province, day of the week, time of day and number of passengers on the motorcycle. This study highlights the extremely low rate of child passenger helmet use in Cambodia, and provides priorities for interventions and enforcement to ensure all children are protected from head injury.
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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 | 0.000 | 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 | 0.001 |
| 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".