Development, Organisation and Implementation of a Surgical Skills ‘Boot Camp’: SIMweek
Bibliographic record
Abstract
BACKGROUND: There is evidence of increased mortality and reduced efficiency in hospitals due to the annual changeover of junior doctors. This paper describes a framework to develop an intensive simulated week that will recreate experiences and situations that junixor surgical interns will likely face in their first weeks after graduation. METHODS: To provide evidence-based recommendations, a systematic review of published literature using the keywords 'surg*', 'boot', 'camp' was performed. Reports of the development, implementation or evaluation of a simulated skills course or 'boot camp' to prepare incoming surgical interns were analysed. RESULTS: Eighteen relevant articles were identified. Subjects on internship preparation courses have identified 'hands-on' training sessions to be very useful. In particular, mock pages have been identified as being valuable and didactic lectures have been identified as the weakest parts of the course. We first consider the end-users of the course and their associated learning needs. We subsequently discuss resources required and propose a strategy for the organisation of a course and selection of teaching faculty. Finally, we consider the costs involved in running a course. CONCLUSIONS: This paper proposes a framework for the development, organisation and implementation of an intensive simulation course to prepare graduating medical students for their role as junior surgical intern. Facilitating the step change in responsibility from student to surgical intern may improve patient safety in addition to reducing the associated anxiety for the clinician.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".