Design and implementation of the 2012 Canadian shoulder course for senior orthopedic residents
Bibliographic record
Abstract
© 2016 Background The objective of the present paper is to analyze the first edition of a comprehensive shoulder course for senior orthopedic surgery residents and the chosen evaluation tools. Hypothesis A course focusing on shoulder surgery, requested by graduating residents in orthopedic surgery, will have a strong level of satisfaction and help improve skills, knowledge, and problem solving abilities in this domain as measured by a pre and post-test. Material and methods A two-day course was created with practical sessions, lectures, and case studies. Participants were given a multiple choice pre and post course test and evaluation questionnaires after each session. Results Sixty residents attended the course. Nine of the fifteen sessions scored above the 90% satisfaction cut-off; none of the sessions scored below 80%. However, only one question showed a statistically significant improvement after the course. Discussion Response to this course was overwhelmingly positive and the sessions received positive evaluations. However, the method to evaluate residents was not adequate; residents reported learning on their freeform evaluations but this was not represented on the multiple choice evaluation method. Evaluation tools and course duration will be modified in future iterations to improve assessment and teaching. Level of evidence IV. Study design Observational.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".