Development of a Competence-Based Spine Surgery Fellowship Curriculum Set of Learning Objectives in Canada
Bibliographic record
Abstract
STUDY DESIGN: Modified-Delphi expert consensus method. OBJECTIVE: The aim of this study was to develop competence-based spine fellowship curricula as a set of learning goals through expert consensus methodology in order to provide an educational tool for surgical educators and trainees. Secondarily, we aimed to determine potential differences among specialties in their rating of learning objectives to defined curriculum documents. SUMMARY OF BACKGROUND DATA: There has been recent interest in competence-based education in the training of future surgeons. Current spine fellowships often work on a preceptor-based model, and recent studies have demonstrated that graduating spine fellows may not necessarily be exposed to key cognitive and procedural competencies throughout their training that are expected of a practicing spine surgeon. METHODS: A consensus group of 32 spine surgeons from across Canada was assembled. A modified-Delphi approach refined an initial fellowship-level curriculum set of learning objectives (108 cognitive and 84 procedural competencies obtained from open sources). A consensus threshold of 70% was chosen with up to 5 rounds of blinded voting performed. Members were asked to ratify objectives into either a general comprehensive or focused/advanced curriculum. RESULTS: Twenty-eight of 32 consultants (88%) responded and participated in voting rounds. Seventy-eight (72%) cognitive and 63 (75%) procedural competency objectives reached 70% consensus in the first round. This increased to 82 cognitive and 73 procedural objectives by round 4. The final curriculum document evolved to include a general comprehensive curriculum (91 cognitive and 53 procedural objectives), a focused/advanced curriculum (22 procedural objectives), and a pediatrics curriculum (22 cognitive and 9 procedural objectives). CONCLUSION: Through a consensus-building approach, the study authors have developed a competence-based curriculum set of learning objectives anticipated to be of educational value to spine surgery fellowship educators and trainees. To our knowledge, this is one of the first nationally based efforts of its kind that is also anticipated to be of interest by international colleagues.
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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 | 0.054 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".