The CEC Model: A Practical Framework for Building Competence, Efficiency, and Confidence in Veterinary Surgical Education
Bibliographic record
Abstract
Veterinary surgical education requires more than technical instruction. To prepare graduates for real-world performance, learners must develop three interdependent domains: competence, efficiency, and confidence. While essential to clinical readiness, these areas are often addressed inconsistently across curricula and teaching environments. This manuscript introduces the Competence, Efficiency, and Confidence (CEC) Model, a practical and experience-based framework designed to support surgical skill development through structured instruction. Developed through over twelve years of teaching in preclinical laboratories, live-animal procedures, and post-graduate training programs, the CEC Model defines competence as consistent, safe surgical technique grounded in sound clinical judgment; efficiency as the ability to perform procedures in an organized, timely, and effective manner; and confidence as the readiness to act independently and adapt under pressure. These domains are cultivated through repeated practice, clear expectations, targeted feedback, and psychologically safe learning environments. Drawing on established educational theory including experiential learning and deliberate practice, the model offers instructional strategies such as scaffolded autonomy, focused coaching, and guided reflection. It bridges the gap between broad competency frameworks and task-specific training tools by offering a developmental structure for surgical education. The CEC Model emphasizes intentional teaching, learner mindset, and relational support as core to surgical growth. Adaptable across clinical, simulated, and post-graduate settings, it offers a practical framework for cultivating technically skilled, efficient, and confident veterinary surgeons, while also opening new opportunities for research on instruction, learner development, and the role of mindset in surgical performance.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".