Use of Seatbelts and Observable Factors among Public Transport Drivers in Addis Ababa, Ethiopia
Bibliographic record
Abstract
Wearing of proper seatbelt while driving is scientifically proven to protect from severe and fatal injuries. The aim of this study was to assess the status of proper seatbelt use and observable factors among public transport drivers in Addis Ababa, Ethiopia. The study conducted an inside vehicle observation study among 600 public transport vehicles in Addis Ababa, Ethiopia, from January to February 2017. Sample vehicles were randomly selected from ten arterial and ten collector road networks. This study used an observational checklist for data collection and logistic regression analysis to find the associated variables with improper seatbelt use. The odds ratio with a 95% CI and a p -value of <0.05 were considered for the statistically significant association. The prevalence of proper seatbelt use was 47.5% [95% CI (43.0–51.3)]. Khat chewing [AOR: 2.41, 95% CI (1.04–5.60)], engaged in driving distraction activities [AOR: 2.93, 95% CI (2.08, 4.13)] and being city bus drivers [AOR: 1.66, 95% CI (1.09, 2.52)], were significantly associated with improper seatbelt use. The actual rate of proper seatbelt use among public transport drivers in Addis Ababa was very low compared with the officially known report. Drivers’ behavior and being drivers of large-sized vehicles were associated with improper seatbelt use.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".