The h-Index of Editorial Board Members Correlates Positively With the Impact Factor of Sports Medicine Journals
Bibliographic record
Abstract
Background: The h-index is a metric widely used to present both the productivity and impact of an author’s previous publications. Purpose: To evaluate and observe any correlations among the h-indices of 2015 editorial board members from 8 top sports medicine journals. Study Design: Systematic review. Methods: The sex, country of residence, degree, and faculty position of the editorial board members were identified using their respective scientific publication profiles. The h-index and other bibliometric indicators of these editorial board members were obtained using both the Web of Science (WoS) and Google Scholar (GS) databases. Nonparametric statistics were used to analyze differences in h-index values, and regression models were used to assess the ability of the editorial board member’s h-index to predict their journal’s impact factor (IF). Results: A total of 422 editorial board members were evaluated. The median h-index of all editors was 20 (interquartile range [IQR], 19) using GS and 15 (IQR, 15) using WoS. GS h-index values were 1.19 times higher than WoS, with significant correlation between these values ( r 2 = 0.88, P = .0001). Editorial board members with a PhD had significantly higher h-indices than those without (GS, P = .0007; WoS, P = .0002), and full professors had higher h-indices than associate and assistant professors (GS, P = .0001; WoS, P = .0001). Overall, there were significant differences in the distribution of the GS ( P < .0001) and WoS ( P < .0001) h-indices of the editorial board members by 2014 IF of the journals. Both the GS h-index (β coefficient, 0.01228; 95% CI, 0.01035-0.01423; P < .0001) as well as the WoS h-index (β coefficient, 0.01507; 95% CI, 0.01265-0.01749; P < .0001) of editorial board members were significant predictors of the 2014 IF of their journal. Conclusion: The h-indices of editorial board members of top sports medicine journals are significant predictors of the IF of their respective journals.
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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 | MetaresearchBibliometrics Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | BibliometricsMetaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.055 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 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, 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".