An Evaluation of h-Index as a Measure of Research Productivity Among Canadian Academic Plastic Surgeons
Why this work is in the frame
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Bibliographic record
Abstract
Background: Evaluation of research productivity among plastic surgeons can be complex. The Hirsch index (h-index) was recently introduced to evaluate both the quality and quantity of one’s research activity. It has been proposed to be valuable in assessing promotions and grant funding within academic medicine, including plastic surgery. Our objective is to evaluate research productivity among Canadian academic plastic surgeons using the h-index. Methods: A list of Canadian academic plastic surgeons was obtained from websites of academic training programs. The h-index was retrieved using the Scopus database. Relevant demographic and academic factors were collected and their effects on the h-index were analyzed using the t test and Wilcoxon Mann-Whitney U test. Nominal and categorical variables were analyzed using χ 2 test and 1-way analysis of variance. Univariate and multivariate models were built a priori. All P values were 2 sided, and P < .05 was considered to be significant. Results: Our study on Canadian plastic surgeons involved 175 surgeons with an average h-index of 7.6. Over 80% of the surgeons were male. Both univariable and multivariable analysis showed that graduate degree ( P < .0001), academic rank ( P = .03), and years in practice ( P < .0001) were positively correlated with h-index. Limitations of the study include that the Scopus database and the websites of training programs were not always up-to-date. Conclusion: The h-index is a novel tool for evaluating research productivity in academic medicine, and this study shows that the h-index can also serve as a useful metric for measuring research productivity in the Canadian plastic surgery community. Plastic surgeons would be wise to familiarize themselves with the h-index concept and should consider using it as an adjunct to existing metrics such as total publication number.
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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 | 0.178 | 0.738 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.089 | 0.147 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 it