International needs analysis in orthopaedic trauma for practising surgeons with a 3-year review of resulting actions
Bibliographic record
Abstract
Introduction: To ensure best-quality education in orthopaedic trauma, the AOTrauma Education Commission conducted a Global Needs Analysis with practising surgeons worldwide.Material and methods: During July to November 2012, an email invitation to complete an online set of 30 questions in eight languages was sent to our members and associates in all countries through AOTrauma’s regional networks. Non-members were invited to participate through collaboration with orthopaedic societies.Results: A total of 3,790 surgeons practising orthopaedic trauma (49%), orthopaedic (15%), general trauma (15%) and specialty orthopaedic (13%) surgeons responded worldwide. Seventy per cent completed all questions, and the top 10 countries accounted for half the responses. The top 3 areas of educational need were orthopaedic trauma, joint replacement and preservation, and pelvis and acetabulum. Aspects influencing likelihood to attend face-to-face courses were: expert faculty, focus on a specific topic, clear objectives, and discussion and feedback from experts. Barriers to attending courses were time away from practice, cost and lack of availability or access.Conclusion: The Global Needs Analysis helped our educational committees to identify short- and mid-term priorities over recent years. Adjustments in our planning have helped meet the needs of our audience on a global, regional and national level.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".