Barriers and facilitators for female practitioners in orthopaedic surgery in Australia
Bibliographic record
Abstract
The Medical Board of Australia reported in March 2023 only 14.9% of surgical consultants are female. Further, only 5% of orthopaedic consultants are female. This is far behind countries such as Canada at 13.6% and the United Kingdom(UK) at 7.3%. Research from the UK, United States of America and Saudi Arabia identified root causes deterring women from specialising in orthopaedics. Factors included false stereotypes engrained in medical schools, the “jock” culture, inflexibility and long hours making orthopaedics appear incompatible for women training during their childbearing years. Research has not been conducted in Australia to identify the specific barriers and, importantly the facilitators, within the nation for women in orthopaedics. Moreover, surgeons’ careers are decades long. Whilst governing bodies have strategies in place to encourage diversity, this will take years to be reflected in statistics. Hence, this project aims to discover the barriers and facilitators women face in the orthopaedic profession both as trainees and as consultants in Australia. Additionally, the project aims to gain insight to orthopaedic surgeon’s perspective on whether sufficient and appropriate strategies are in place to reach gender parity in the near future.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".