Procedural skills training for Canadian medical students participating in international electives
Bibliographic record
Abstract
BACKGROUND: International medical electives (IMEs) are unique learning opportunities; however, trainees can risk patient safety. Returning medical students often express concern about doing procedures beyond their level of training. The Canadian Federation of Medical Students has developed guidelines for pre-departure training (PDT), which do not address procedural skills. The purpose of this research is to determine which procedural skills to include in future PDT. METHODS: Twenty-six medical students who returned from IMEs completed surveys to assess PDT. Using a Likert scale, we compared procedures performed by students before departing on IME to those performed while abroad. We used a similar scale to assess which procedures students feel ought to be included in future PDT. RESULTS: There was no significant increase in number of procedures performed while on IME. Skills deemed most important to include in future PDT were intravenous line insertion, suturing of lacerations, surgical assisting and post-operative wound care. CONCLUSIONS: Pre-departure training is new and lacks instruction in procedural skills. Over half the students rated several procedural skills such as IV line insertion, suturing, assisting in surgery, post operative wound management and foley catheterization as important assets for future PDT.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".