State of Gender Diversity and Equity Policies within Plastic and Reconstructive Surgery in Canada
Bibliographic record
Abstract
BACKGROUND: Given the growing number of women in plastic and reconstructive surgery (PRS), it is imperative to evaluate the extent of gender diversity and equity policies among Canadian PRS programs to support female trainees and staff surgeons. METHODS: A modified version of the United Nations Women's Empowerment Principles (WEPs) Gender Gap Analysis tool was delivered to Canadian PRS Division Chairs (n = 11) and Residency Program Directors (n = 11). The survey assessed gender discrimination and equity policies, paid parental leave policies, and support for work/life balance. RESULTS: Six Program Directors (55% response rate) and ten Division Chairs (91% response rate) completed the survey. Fifty percent of respondents reported having a formal gender non-discrimination and equal opportunity policy in their program or division. Eighty-three percent of PRS residency programs offered paid maternity/paternity/caregiver leave; however, only 29% offered financial or non-financial support to its staff surgeons. Only 33% of programs had approaches to support residents as parents and/or caregivers upon return to work. Work/life balance was supported for most trainees (67%) but only few faculty members (14%). CONCLUSIONS: The majority of Canadian PRS programs have approaches rather than formal policies to ensure gender non-discrimination and equal opportunity among residents and faculty. Although residency programs support wellness, few have approaches for trainees as parents and/or caregivers upon return to work. At the faculty level, approaches and policies lack support for maternity/paternity/caregiver leave or work/life balance. This information can be used to develop policy for support of plastic surgery trainees and faculty.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".