Gender associations with selection into Australian Orthopaedic Surgical Training: 2007–2019
Bibliographic record
Abstract
BACKGROUND: Communities have better health outcomes when their clinicians reflect the diversity of the communities they serve. More than 50% of Australian medical school graduates are female, yet women represent less than 5% of Australian orthopaedic surgeons. Selection into orthopaedic surgical training in Australia is an annual, nation-wide process, based on curriculum vitae (CV), referee reports and performance in multiple mini-interviews (MMI). The influence of applicant gender on these selection scores was examined. METHODS: The CV, referee reports and MMI scores used for selection for each year from 2007 to 2019 were analysed from the perspective of the applicant's gender. RESULTS: Over the years of the study, male applicants had higher CV scores and referee report scores, which determined the gender proportions invited to interview. By contrast, the interview process and selection from interview did not demonstrate a gender association. CONCLUSION: We describe the impact of selection tools, utilized over the past 13 years, on the gender diversity of trainees commencing orthopaedic surgery training in Australia. Leaders in postgraduate training should examine commonly used selection procedures to identify and reduce the unconscious biases that may affect their performance and value.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".