Publication Productivity Among Academic Orthopaedic Surgeons in Canada
Bibliographic record
Abstract
Objective The Hirsch Index (h-index) and m-index are often utilized to assess academic productivity and have been widely found to have a positive association with academic promotion and grant selection. The aim of this study was to assess the relationship between these indices and academic ranks among Canadian orthopaedic surgery faculty members. Methods Five hundred and sixty-seven Canadian orthopaedic surgery faculty members associated with residency training programs were included in the study. H-indices of individual faculty members were obtained through Elsevier's Scopus database. Faculty members' year of residency graduation was recorded from their respective licensing body database and was utilized as a surrogate for the start of their academic career to determine career duration and calculate the m-index. Faculty members were divided based on their academic rank (assistant, associate and full professors) and subspecialty. Results Increased h-index, m-index and long career duration were associated with increased academic rank, while gender did not demonstrate an association. Overall, males had a significantly higher h-index compared to females, but no significant difference was observed when comparing the m-index between genders. The m-index varied between subspecialties among senior faculty, but not among junior-ranked faculty. Conclusion Bibliometric academic productivity using h-index and m-index is associated with academic ranking among Canadian orthopaedic surgeons at training institutions. Although these indices may provide insight into the academic merits of faculty members, caution must be taken about utilizing it indiscriminately and their limitations must be strongly considered.
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How this classification was reachedexpand
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.025 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".