Improving Equity, Diversity, and Inclusion in Plastic, Reconstructive, and Aesthetic Surgery in Canada: A Call to Action—Part II
Bibliographic record
Abstract
Background: In May 2022, we challenged our colleagues to evaluate their educational approaches, policies, recruitment strategies, and leadership organizations with an Equity, Diversity, and Inclusion (EDI) lens. Methods: Two virtual national round table meetings were held in 2023 to discuss approaches to integration of the EDI principles into current Canadian plastic surgery training programmes. Additionally, integrative documents and processes were established within our programme to act as a guide for integration of the principles of EDI in the areas of resident education, recruitment, and retention. Results: There is an increasing awareness amongst Canadian plastic surgeons of the importance of integrating EDI education into our plastic surgery training programmes, yet there is a lack of experience and/or lack of resources available to facilitate these changes. Our taskforce (Division of Plastic Surgery at the University of Toronto) implemented an EDI curriculum in our programme in 2 main domains: Education and Recruitment Strategies. Conclusions: Breaking down some of the long-entrenched inequities in our healthcare system is an ongoing process. More work needs to be done toward increasing our trainee and faculty exposure to EDI principles, so that they can integrate these skills into their clinical practice, leadership, and beyond. Our taskforce’s successes and challenges can act as a useful resource to other programmes desiring to initiate similar change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.009 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".