The Importance of Determining Trainee Perspectives on Procedural Competencies During Spine Surgery Clinical Fellowship
Bibliographic record
Abstract
Study Design: Longitudinal survey. Objective: It remains important to align competence-based objectives for training as deemed important by clinical fellows to those of their fellowship supervisors and program educators. The primary aim of this study was to determine trainee views on the relative importance of specific procedural training competencies. Secondarily, we aimed to evaluate self-perceived confidence in procedural performance at the commencement and completion of fellowship. Methods: Questionnaires were administered to 68 clinical fellows enrolled in the AOSNA fellowship program during the 2015-2016 academic year. A Likert-type scale was used to quantify trainee perspectives on the relative importance of specific procedural competencies to their training base on an established curriculum including 53 general and 22 focused/advanced procedural competencies. We measured trainee self-perceived confidence in performing procedures at the commencement and completion of their program. Statistical analysis was performed on fellow demographic data and procedural responses. Results: Our initial survey response rate was 82% (56/68) and 69% (47/68) for the follow-up survey. Although most procedural competencies were regarded of high importance, we did identify several procedures of high importance yet low confidence among fellows (ie, upper cervical, thoracic discectomy surgery), which highlights an educational opportunity. Overall procedural confidence increased from an average Likert score of 4.2 (SD = 1.3) on the initial survey to 5.4 (SD = 0.8) by follow-up survey ( P < .0001). Conclusions: Understanding trainee goals for clinical fellowship remains important. Identification of areas of low procedural confidence and high importance to training experience will better guide fellowship programs and supervisors in the strategic delivery of the educational experience.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".