Spinal Surgery Fellowship Education in Canada
Bibliographic record
Abstract
STUDY DESIGN: A cross-sectional survey of spine surgery fellowship educators and trainees. OBJECTIVE: To determine educator and trainee perspectives on the relative importance of core cognitive and procedural competencies in fellowship training. To determine perceptions of confidence in competencies by trainees near the end of their fellowship. Finally, to determine potential differences comparing surgeons by background specialty training (neurosurgical or orthopedic) of their views on competencies. SUMMARY OF BACKGROUND DATA: Spine surgery is a growing subspecialty with increasing collaboration among specialists of varied specialty backgrounds involved in education. With the recent implementation of competency-based curricula during specialty training, opportunities may exist in enhancing fellowship education. METHODS: A questionnaire on cognitive and procedural competencies was administered (online and paper) to fellowship educators and trainees across Canada. A follow-up questionnaire was administered to nonresponders 3 months later. Survey results were summarized using qualitative and descriptive statistics with comparative analyses performed. RESULTS: Of the identified respondents, the response rate was 91%, (15/17 fellow trainees; 47/51 educators). Twelve of the 13 core cognitive skill categories were rated as being important to acquire by the end of fellowship. Trainees were not comfortable performing, and requested additional training in 8 of the 29 less common and technically demanding procedural skills. There were different perceptions on the relative importance of competencies comparing trainees by specialty background as well as different perceptions on the types of competencies where additional training was desired to achieve competency (P < 0.05). Fellowship educators and trainees possessed similar perceptions on the relative importance of core cognitive and procedural competencies required for successful training. CONCLUSION: Background specialty influenced the perceptions of both fellowship educators and trainees. This study identified potential gaps or perceived deficiencies in the competency of current fellows. Improvements in spine fellowship education should target these areas through developing evidence-based curriculum changes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".