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Record W6943805854 · doi:10.17605/osf.io/2tbc7

Barriers and facilitators for female practitioners in orthopaedic surgery in Australia

2024· article· en· W6943805854 on OpenAlexaboutno aff

Bibliographic record

VenueOSF Preprints (OSF Preprints) · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsOrthopedic surgeryFace (sociological concept)Perspective (graphical)Developed countryMEDLINE

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.764
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0930.033

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.

Opus teacher head0.036
GPT teacher head0.319
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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