Variability in research productivity among Canadian surgical specialties
Bibliographic record
Abstract
Background: Academic productivity, as measured by number and impact of publications, is central to the career advancement and promotion of academic surgeons. We compared research productivity metrics among specialties and sought factors associated with increased productivity. Methods: Academic surgeons were identified through departmental webpages and their scholarly metrics were collected through Scopus in a standardized fashion. We collected total number of documents, h-index, and average number of publications per year in the preceding 5 years. We explored whether presence of a training program, graduate degree, academic rank and size of the clinical group affected productivity metrics. Linear regression was used for multivariable analysis. Results: We collected data on 2172 surgeons from 15 separate academic centres across Canada. Wide variability existed in metrics among specialties, with cardiac and neurosurgery being the most productive, and vascular surgery and plastic surgery being the least productive. The average number of publications was 71, and the average h-index was 18.7. The average h-index for cardiac surgery was 25.7 compared with 8.3 for vascular surgery (p < 0.001). Our multivariable model identified academic rank, surgical specialty, graduate degree, presence of a training program, and larger clinical group as being associated with increased academic productivity. Conclusion: There is variability in research productivity among Canadian surgical specialties. Cardiac surgery and neurosurgery are productive, whereas vascular surgery and plastic surgery are less productive than other surgical disciplines. Obtaining a research-oriented graduate degree, being part of a larger clinical group, and presence of a training program were all associated with higher productivity, even after adjusting for academic rank and specialty.
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How this classification was reachedexpand
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 | 0.028 | 0.203 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".