Traffic medicine–related research: a scientometric analysis
Bibliographic record
Abstract
OBJECTIVE: Traffic crashes and related injuries are important causes of morbidity and mortality and impose insofar an important burden on public health. However, research in this area is often under-funded. The aim of this study was to analyse quantity, evolution and geographic distribution of traffic medicine-related research. This multi-sectorial field covers both transport and health care sectors. DESIGN: A scientometric approach in combination with visualizing density equalizing mapping was used to analyse published data related to the field of traffic medicine between 1900 and 2008 within the "Web of Science" (WoS) database. RESULTS: In total, 5,193 traffic medicine-associated items were produced between 1900 and 2008. The United States was found to have the highest research activity with a production of n = 2,330 published items, followed by Germany (n = 298) and Canada (n = 219). Cooperation analyses resulted in a peak of published multilateral cooperations in the year of 2003. The country with the highest multilateral activity was the USA. The average number of cited references per publication varied heavily over the last 20 years with a maximum of 27.67 in 1995 and a minimum of 15.08 in 1998. Also, a further in-depth analysis was performed with a focus solely on public health aspects which revealed similar trends. CONCLUSIONS: Summarizing the present data it can be stated traffic medicine-related research productivity grows annually. Also, an active networking between countries is present. The data of the present study may be used by scientific organisations in order to gain detailed information about research activities in this field which is extremely important for public health.
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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 | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.026 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.128 | 0.176 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".