Examining the equity and diversity characteristics of academic general surgeons hired in Canada
Bibliographic record
Abstract
Background: Job competition and underemployment among surgeons emphasize the importance of equitable hiring practices. The purpose of this study was to describe some of the demographic characteristics of academic general surgeons and to evaluate the gender and visible minority (VM) status of those recently hired. Methods: Demographic information about academic general surgeons across Canada including gender, VM status, practice location and graduate degree status was collected. Location of residency was collected for recently hired general surgeons (hired between 2013 and 2020). Descriptive statistics were performed on the demographic characteristics at each institution. Pearson correlation coefficients and hypothesis testing were used to determine the correlation between various metrics and gender and VM status. Results: A total of 393 general surgeons from 30 academic hospitals affiliated with 14 universities were included. The percentage of female general surgeons ranged from 0% to 47.4% and the percentage of VM general surgeons ranged from 0% to 66.7% at the hospitals. This heterogeneity did not correlate with city population (gender: r = 0.06, p = 0.77; VM: r = 0.04, p = 0.83). The percentage of VM general surgeons at each hospital did not correlate with the percentage of VM population in the city (r = 0.13, p = 0.49). Only 34 of 120 recently hired academic general surgeons (28.3%) did not have a graduate degree. The percentage of recently hired academic general surgeons who did not have a graduate degree was approximately 1.5 times higher among male hirees than female hirees. With respect to academic promotion, the percentage of female full professors ranged from 0% to 40.0% and did not correlate with the percentage of female general surgeons at each institution (r = 0.11, p = 0.70). The percentage of VM full professors ranged from 0% to 44.4% and was moderately correlated with the percentage of VM surgeons at each institution (r = 0.40, p = 0.16). Conclusion: The academic general surgery workforce appears to be somewhat diverse. However, there was substantial heterogeneity in diversity between hospitals, leaving room for improvement. We must be willing to examine our hiring processes and be transparent about them to build an equitable surgical workforce.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".