Assessing Factors Associated with Non-Fatal Injuries from Road Traffic Accidents among Malaysian Adults: A Cross-Sectional Analysis of the PURE Malaysia Study
Bibliographic record
Abstract
Non-fatal injuries (NFIs) due to road traffic accidents (RTAs) are a public health problem worldwide that significantly impacts the population morbidity and healthcare costs. As the demands for vehicles in developing countries, such as Malaysia, is increasing annually, the present study aims to determine the prevalence and factors associated with NFIs due to RTAs among Malaysia's adult population. METHODS: This was a cross-sectional study involving 15,321 participants from the Prospective Urban and Rural Epidemiological (PURE) study conducted in Malaysia. Participants reported whether they had experienced an NFI that limited their normal activities within the past 12 months. Data on risk factors for NFIs were elicited. Multiple logistic regression models were fitted to identify the associated factors. RESULTS: Overall, 863 participants (5.6% of 15,321) reported at least 1 NFI in the past 12 months, with 303 caused by RTAs (35.1%), 270 caused by falls (31.3%) and 290 attributed to other causes (33.6%). The factors associated with higher odds of sustaining an NFI due to an RTA were being male (adjusted odd ratio (AOR) 2.08; 95% CI 1.33-3.26), having a primary (2.52; 1.40-4.55) or secondary (2.64; 1.55-4.49) level of education, being overweight to obese (1.40; 1.01-1.94), being currently employed (2.03; 1.31-3.13) and not practicing a noon nap/siesta (1.38; 1.01-1.89). CONCLUSIONS: The occurrence of NFIs due to RTAs is highly preventable with strategic planning aimed at reducing the risk of RTAs among the Malaysian population. Interventions focusing on protecting road users, especially those who drive two-wheelers, with proactive road safety awareness and literacy campaigns, combined with strict enforcement of the existing traffic laws and behavioural modifications, may reduce the risk of NFIs following RTAs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".