Tying measurement to action in equity, diversity, and inclusion work in academic surgical departments
Bibliographic record
Abstract
Background: Strategies to address inequities, bias, and discrimination that disadvantage Canadian physicians from marginalized groups are urgently needed. We describe a multilevel needs assessment of equity, diversity, and inclusion (EDI) in 2 departments of surgery that focused on identifying evidence-based interventions. Methods: We invited members of the departments of surgery at the University of Calgary and the University of Saskatchewan to complete the Diversity Engagement Survey (DES), a 22-item instrument designed to understand workplace engagement and inclusion among physicians, with higher scores indicating greater engagement and inclusion. Leaders completed a Leadership EDI Readiness Assessment to understand their own barriers to EDI work and an Organizational EDI Readiness Assessment to understand structures for EDI in their division. Leaders were provided resources and interventions to address the identified gaps in these assessments. Results: The most common organizational gaps in structures for EDI work in surgical divisions and training programs (n = 34, 37.4%) were in community outreach and measurement and reporting. Surgeons who identified as cisgender men (n = 101) felt more engaged and included than those who identified as cisgender women (n = 43; 3.81 [standard deviation (SD) 0.73] v. 3.51 [SD 0.78]; p = 0.04). White cisgender men (n = 66) had the highest feelings of engagement and inclusion (mean 3.95 [SD 0.62]). Participating surgical sections and training programs were directed to evidence-informed initiatives to improve community outreach and measurement and reporting to address EDI in their settings. Conclusion: Our findings support that gender and racial or ethnic identities influence the workplace experiences of surgeons in Canada. A multilevel approach to EDI work in surgical departments can direct leaders to areas for intervention.
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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 | 0.138 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.003 | 0.007 |
| 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".