Association between academic degrees and research productivity: an assessment of Canadian academic general surgeons
Bibliographic record
Abstract
Background: For academic hiring committees and surgical trainees, the benefits of a graduate degree are unclear. We sought to identify if graduate degrees or professorship status were associated with increased research productivity among Canadian academic surgeons. Methods: We included general surgeons from the largest hospitals associated with accredited residency programs. We classified staff surgeons active between 2013 and 2018 by degree (MD only, master’s degree, PhD) and professorship (assistant, associate, professor) status. We identified their publications from January 2013 to December 2018. Variables of interest included publications per year, citations per article, journal of publication, CiteScore, author’s Hirsch (h) index and the revised h-index (r-index). We used Kruskal–Wallis tests and the Dunn multiple comparison test to assess statistical significance. Results: We identified 3262 publications from 187 surgeons, including 78 (41.7%) with no graduate degree, 84 (44.9%) with master’s degrees and 25 (13.4%) with PhDs. Surgeons with graduate degrees had more publications per year, higher CiteScores, more citations per article, and higher h- and r-indices than those without graduate degrees. Surgeons with doctorates had the highest median values in all domains, but differences were not significant compared with surgeons with master’s degrees. Seventy-seven (41.8%) surgeons were assistant professors, 63 (34.2%) were associate professors and 44 (23.9%) were full professors. Statistically, full professors had a greater number of publications per year and higher h- and r-indices than their counterparts. Conclusion: Surgeons with graduate degrees or more advanced professorships had the greatest research productivity. Surgeons with doctorates trended toward greater research productivity than those holding master’s degrees.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.017 | 0.027 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".