Australian orthopaedic surgery training: Australian Orthopaedic Association's strategic education review
Bibliographic record
Abstract
BACKGROUND: The Australian Orthopaedic Association (AOA) is the peak professional body for orthopaedic surgeons in Australia and is responsible for their training and continuing medical education. The 2012 AOA strategic education review employed a gap analysis to identify issues and generate a roadmap for the enhancement of AOA's education and training activities. This paper describes the process and outcomes of this review, which led to the 'AOA 21' Education Re-design Project, and the review's relevance to surgical education in Australia. METHODS: The AOA strategic education review was undertaken by international clinician education and orthopaedic experts, and involved AOA staff, fellows and trainee members throughout Australia. This review progressed through four phases: a review of current training; a global 'best-practice' perspective; a set of aspirational goals; and a roadmap for implementation. RESULTS: Seven key strengths (e.g. camaraderie in training and surgical skills teaching) and 15 challenges (e.g. 'non-technical' skills teaching, subjective methods of decision making and inadequate assessment practices) were identified in the review stage. Sixteen major recommendations for improvements incorporating current or emerging world's best education practices were targeted in an 8-year implementation framework. CONCLUSION: The AOA's strategic education review serves as a roadmap for other specialty training organizations to review their programmes and implement contemporary best practices in surgical training. The AOA 21 project has positioned orthopaedic surgery as a leader in competency-based surgical education and training in Australia.
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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 | 0.019 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".