Trends in types of graduate degrees and research output for academic general surgeons in Canada
Bibliographic record
Abstract
Summary The proportion of general surgeons with graduate degrees in Canada is increasing. We sought to evaluate the types of graduate degree held by surgeons in Canada, and whether differences in publication capacity exist. We evaluated all general surgeons working at English-speaking Canadian academic hospitals to determine the types of degrees achieved, changes over time and research output associated with each degree. We identified 357 surgeons, of whom 163 (45.7 %) had master’s degrees and 49 (13.7 %) had PhDs. Achievement of graduate degrees increased over time, with more surgeons earning master’s degrees in public health (MPH), clinical epidemiology and education (MEd), and fewer master’s degrees in science (MSc) or PhDs. Most publication metrics were similar by degree type, but surgeons with PhDs published more basic science research than those with clinical epidemiology, MEd or MPH degrees (2.0 v. 0.0, p < 0.05); surgeons with clinical epidemiology degrees published more first-author articles than surgeons with MSc degrees (2.0 v. 0.0, p = 0.007). An increasing number of general surgeons hold graduate degrees, with fewer pursuing MSc and PhD degrees, and more holding MPH or clinical epidemiology degrees. Research productivity is similar for all groups. Support to pursue diverse graduate degrees could enable a greater breadth of research.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".