Estimate of the magnitude of risky and protective behaviors associated with road traffic injuries in capitals participating in the Life in Traffic Project of Brazil
Bibliographic record
Abstract
BACKGROUND: Brazil occupies the fifth position in the ranking of the highest mortality rates due to RTI in the world. With the objective of promoting traffic safety and consequently reducing deaths, Brazil created the Life in Traffic Project (LTP). The main goal of LTP is reducing 50% of RTI deaths, by promoting interventions to tackle risk factors, such as driving under the influence of alcohol and excessive and/or inappropriate speed. Thus, the aim of this study was to estimate the magnitude of risky and protective factors for RTI in capitals participating in the LTP in Brazil. We estimated these factors according to sociodemographic (age group, sex, education, race and, type of road user). METHODS: A total of 5,922 car drivers and motorcyclists from 14 Brazilian capitals participating in the LTP were interviewed. Data collection was carried out in sobriety checkpoints at night and consisted of the administration of an interview and a breathalyzer test. Risky and protective behaviors associated with RTI were investigated. Covariates of the study were: age, sex, education, race and, type of road user. Poisson multiple regression analysis was used to assess the relationship between variables of interest. RESULTS: The prevalence of individuals with positive blood alcohol concentration (BAC) was 6.3% and who reported driving after drinking alcohol in the last 30 days was 9.1%. The others risky behaviors reported were: driving at excessive speed on roads of 50 km/h, using a cell phone for calls while driving, using a cell phone to send or read calls while driving, running a red light. Use of seatbelts and helmets showed prevalence above 96,0% Use of seatbelts showed prevalence of 98.6% among car drivers, and helmet use was described by 96.6% of motorcycle drivers. Most risky behaviors were more prevalent in younger age groups (except BAC measurement higher in older participants), in males (except for cell phone use), in participants with higher education level and without a driver's license. CONCLUSION: Excessive speed and driving under the influence of alcohol, defined as priorities within the LTP, need more consistent interventions, as they still have considerable prevalence in the cities investigated. The factors described such as cell phone usage and passing red traffic lights should also need to be prioritized as a focus on promoting traffic safety.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".