Diversity of physicians in leadership and academic positions in Alberta: a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Efforts to reduce barriers and disparities faced by marginalised physicians are limited by a lack of data on the current diversity of the Canadian physician workforce. We aimed to characterise the diversity of the Albertan physician workforce. METHODS: This cross-sectional survey, open to all Albertan physicians from 1 September 2020 to 6 October 2021, measured the proportion of physicians from traditionally under-represented groups, including those with diverse gender identities, disabilities and from racial minorities. RESULTS: There were 1087 respondents (9.3% response rate); of whom 33.4% identified as cisgender men (n=363), 46.8% as cisgender women (n=509) and less than 3% as gender diverse. Fewer than 5% were members of the LGBTQI2S+community. Half were white (n=547), 4.6% were black (n=50) and fewer than 3% were Indigenous or Latinx. Over one-third reported a disability (n=368, 33.9%). There were 303 white cisgender women (27.9%), 189 white cisgender men (17.4%), 136 black, Indigenous or person of colour (BIPOC) cisgender men (12.5%) and 151 BIPOC cisgender women (13.9%). Compared with BIPOC physicians, white participants were over-represented in leadership positions (64.2% and 32.1%; p=0.06) and academic roles (78.7% and 66.9%; p<0.01). Cisgender women had less often applied for academic promotion than cisgender men (85.4% and 78.3%, respectively, p=0.01), and BIPOC physicians had been denied promotion more frequently (7.7% compared with 4.4%; p=0.47). CONCLUSION: Many Albertan physicians may experience marginalisation through at least one protected characteristic. There were race-based and gender-based differences in experiences of medical leadership and academic promotion which may explain observed disparities in these positions. To increase diversity and representation in medicine, medical organisations should focus on inclusive cultures and environments. Universities should focus efforts on supporting BIPOC physicians, especially BIPOC cisgender women, in applying for promotion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".