Bibliometric analysis of Traffic Medicine – related publications: 2008 – 2015
Bibliographic record
Abstract
Background: A pproximately 1.25 million deaths annually due to road traffic accidents (RTA). Research has proven to improve traffic safety. Therefore, this study was carried out to analyze data in the field of traffic medicine to endorse and support the Decade of Action for Road Safety plan, and to enrich the literature in the field of RTA. Method: Scopus database was used to retrieve relevant data, then it was refined to traffic medicine field. Bibliometric indicators were presented and data visualization was carried out using VOSviewer and ArcGIS10.1 Results: A total of 2029 traffic medicine–related publications were retrieved, with h -index of 46. The relative growth rate declined from 0.63 in 2008 to 0.18 in 2015 while doubling time increased from 1.1 in 2008 to 3.55 in 2015. In 2008, traffic medicine-related publications were approximately 70% of total publications on traffic accidents and dropped to approximately 67% in 2015. Retrieved documents had a total of 8,478 authors, from 101 different countries with the USA having the largest share (498; 25.54%). Sweden, the UK and Canada had the highest percentage of inter-country collaboration. The most active institution was the University of Toronto (36 documents). Accident Analysis and Prevention was the most preferred for publishing RTA documents. Conclusion: The study period was characterized by slow growth of traffic medicine – related publications, which may suggest relative lack of interest or funding. To reverse fatalities due to RTA and injuries, researchers need to be more involved in RTA research to present solutions for better safety.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.144 | 0.171 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".