Racial diversity amongst Otolaryngology-Head and Neck Surgery programs in Canada
Bibliographic record
Abstract
Abstract Background The Canadian landscape of racial diversity in academic OHNS programs is currently unknown, as to date Canadian medical organizing bodies have refrained from collecting race-based data. However, new policy guidelines by the Canadian Medical Association support the collection of data that may be used to support equity, diversity and inclusion programs. This study aims to describe the representation of visible minorities amongst academic OHNS departments and divisions in Canada at various levels of academic seniority. Methods An online survey was distributed to members of the 13 academic OHNS department in Canada in 2022. The survey collected demographic data as well as each participant’s self-reported race and gender. The primary outcome was the comparison of the racial demographics of Canadian academic OHNS programs to Canadian census data. Secondary outcome measures assessed how demographics varied based on academic position and gender. Simple descriptive statistics were tabulated for all demographic variables. Chi-square goodness of fit analysis was used to compare survey results to anticipated demographics based on 2016 Canadian census data. Results Of 545 surveys distributed, 224 surveys were completed (response rate of 41%); 67.9% or respondents were male and 32.1% were female. Of these respondents, 71 were residents, 26 lecturers, 54 assistant professors, 39 associate professors, and 34 full professors. There was significantly greater minority representation amongst residents (47.9%), assistant professors (39.6%), and lecturers (40.7%) compared to the Canadian population (25.3%) p < 0.001. Results also showed that there were significantly fewer female lecturers (25.9%, p = 0.01), assistant professors (31.5%, p = 0.006), and full professors (2.9%, p < 0.001) compared to an assumed even proportion of men and women in the population. Conclusions Academic OHNS programs in Canada are more racially diverse than the Canadian population. However, women continue to be under-represented in more senior positions, especially women who are visible minorities. Further investigation into the systemic factors that may contribute to this disparity is needed as well as effective ways to promote diversity amongst academic OHNS departments at all levels of academic seniority. Graphical abstract
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".