A roadmap for surgeon leaders in improving gender equity: educational strategies, implementation, and evaluative methods
Bibliographic record
Abstract
BACKGROUND: Gender diversity is lacking in the orthopedic workforce, and patient outcomes are known to be negatively affected when gender inequity exists. Following an unpublished needs assessment, we sought to evaluate participants' proposed solutions to gender inequity faced by female orthopedic surgeons in Canada and to translate the range of solutions into a medical education model. METHODS: Open-text responses from a gender-bias survey of Canadian orthopedic surgeons who identified as women were analyzed qualitatively by 2 experts. The questions covered the domain of changes required to improve the work environment. We used the latter 2 steps of Kern's educational framework as a lens to interpret the data and generate solutions. RESULTS: A total of 330 eligible surgeons were approached, and 220 (67.0%) completed the survey. Respondents provided more than 14 000 words of text for analysis. Using the themes of the unpublished needs assessment, we defined broad goals and specific objectives, including raising awareness, establishing an equitable playing field, drawing attention to male privilege, developing effective mentorship, eliminating harassment, and unburdening the second shift. We present solutions via educational strategies and evaluative methods based on Kern's framework. CONCLUSION: We offer a road map for improving gender diversity in orthopedic surgery, based on survey results from Canadian women in orthopedic surgery, analyzed using a gender bias framework and an educational conceptual framework. We hope that this work will improve the surgical profession and patient care.
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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 | 0.369 | 0.241 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".