Analysis of h-index and other bibliometric markers of productivity and repercussion of a selected sample of worldwide emergency medicine researchers
Bibliographic record
Abstract
Objective To explore bibliometric markers in a worldwide sample of emergency physician investigators to define global, continental and individual patterns over time. Methods We evaluated the number of papers published, citations received, cumulative impact factor and h-index of editorial board members of six international emergency medicine journals. We calculated the individual values for every year of each author's career to evaluate their dynamic evolution. We analysed the results by researcher world area and growth rate. Results We included 107 researchers (76 American, 21 European and 10 Australasian; 46 slow-rate -group C-, 43 medium-rate -group B- and 18 fast-rate growth -group A-). The median experience was 18 (IQR: 12) years, without subgroups differences. Dynamic analysis over time showed good fit with quadratic function in all individual researchers and for all bibliometric markers (R2: 0.505–0.997), with the h-index achieving the best R2. The combined analysis of the h-index of the 107 investigators also fit the quadratic model (R2=0.49). Analysis by predefined continental and growth-rate subgroups allowed defining specific patterns (R2between 0.46–0.54 and 0.80–0.86, respectively): by continents, American researchers' h-index increased 0.632 points per year, European 0.417 and Australasian 0.341; by growth rate, researchers from group A, B and C increased 1.239, 0.683 and 0.320, respectively. Conclusions Dynamic analysis of every individual author indicator over time has a very good fit with a quadratic model, with the h-index achieving the best R2. It is also possible to construct models based on continent and rate of growth that could help to predict future expected outcomes of researchers in a particular subgroup and to classify new emerging researchers by growth rate.
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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 | Observational | 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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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.
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".