Association of Social Determinants of Health and Road Traffic Deaths: A Systematic Review.
Bibliographic record
Abstract
OBJECTIVE: This study aims to review systematically the association of social determinants of health (SDH) and road traffic deaths (RTD) within scientific literature. METHODS: A search strategy was designed and run in EMBASE, PubMed via MEDLINE, Scopus, Web of Science, and Cochrane library. Through title, abstract, and full-text screening, all English original papers (except ecological studies) which studied social determinants of health and fatal injuries were included. Papers which studied association between RTD and the education, income, rural settlement, and marital status were evaluated and the related data was extracted from the full-texts. RESULTS: Eleven articles out of 7,897 primary results were selected to be included in the study. Among eight papers studied education, seven confirmed a negative association between years of schooling and RTD. Two out of three articles reported no association between income leveland RTD. Among three papers studied rural settlement, two approved a positive relationship between this determinant and RTD. Both articles studied marital status, confirmed an association between this determinant and RTD. CONCLUSION: A few papers studied association of social determinants of health (SDH) and RTD. There was an inverse relationship between education and RTD. The evidence for such an association between income, rural settlement, and marital state was scarce. Further investigations are recommended through original research.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".