Enhancing Pre-clerkship Students' Readiness for Surgery: A Kern's Framework-Guided Workshop
Bibliographic record
Abstract
BACKGROUND: The transition from preclinical years to surgical clerkship is challenging, as traditional curricula emphasize didactic learning over technical and interpersonal skills. We developed a novel, structured workshop based on Kern's Six-Step Guide to Curriculum Design to enhance medical learners' clerkship readiness. MATERIALS AND METHODS: Conducted in 2023 and 2024 at a single Canadian institution, the workshop featured five stations: Introduction to the OR, The Surgical Ward, The Surgical Consult, Technical Skills, and Thriving in Surgical Clerkship. A multidisciplinary team of surgeons, trainees, nurses, and a clinical clerk facilitated clinical vignettes, small-group discussions, and operative simulations. Learners' confidence and knowledge were assessed through pre- and postworkshop questionnaires, and suturing skills were evaluated using a validated tool. Comparative analyses were performed using Paired T-tests and Wilcoxon signed-rank tests. RESULTS: Fifty-nine (95.2%) medical students were included in the analysis after removing incomplete responses. Forty-one students (69.0%) had little (less than five times) or no exposure to an OR in the last year. Learners' overall median confidence improved significantly post-workshop [2.0 (IQR: 2.1-3.2) versus 6.4 (IQR: 6.3-6.8) P = 0.005], as did their suturing skills (11 ± 4.8 versus 23 ± 2.4, P < 0.0001). Fifty-one learners (86.4%) agreed that the workshop decreased their anxiety around clerkship. All agreed that the workshop should be offered again. CONCLUSIONS: Our workshop effectively addressed gaps in surgical education by applying Kern's framework, near-peer teaching, and simulation-based learning. The curriculum combined theoretical knowledge and clinical skills, thereby significantly improving clerkship preparedness and serves as a scalable model for surgical education.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".