Comparing road traffic injury datasets in the Dominican Republic with Health Organisation recommendations
Bibliographic record
Abstract
Background Police datasets are commonly used to estimate Road Traffic Injury (RTI) burden. Nonetheless, newer health system—based datasets such as social security (SS) data are now becoming available in developing countries. Aims/Objectives/Purpose To compare availability of information in police and health-based RTI datasets with WHO recommendations in a developing country. Methods Study setting was the Dominican Republic (DR) in 2010. Availability of RTI information was assessed in three datasets: Police, SS, and Forensic Agency. Content and quality were compared with definitions of the 21 recommended core variables of the WHO Road Safety Data Systems Manual. Results/Outcomes The three databases included 14 266 records. Availability of age, sex, intent, location, nature and mechanism of injury, and hour and date was higher in the SS dataset (100%) than in police records, where availability varied from 56% for vehicle mark to 86% for age and 83% for road type. Essential identifier variables such as the national number was never recorded in Forensic records, or poorly documented (30%) in police data. Many variables related to human and exposure factors, such as alcohol consumption, seat-belt wearing, and helmet utilisation or traffic volume were not recorded. Significance/Contribution to the Field Many relevant variables are available in SS records or could be added inexpensively into the current datasets. DR authorities should adopt urgently the entire WHO minimal data systems recommendation to develop a reliable, affordable and accurate RTI monitoring system.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".