Non-technical skills of Norwegian medical students at different training sites: a comparative, observational cohort study
Bibliographic record
Abstract
PURPOSE: Mastering non-technical skills (NTS) is a fundamental part of the training of new physicians to perform effectively and safely in the medical practice environment. Ideally, they learn these skills during medical school. Decentralized medical education is being implemented increasingly worldwide. Two of the three training sites studied, Bodø (a regional hospital) and Finnmark (a rural local hospital), implemented decentralized medical education. The third training site was the main campus in Tromsø, located at an urban university hospital. The training in Finnmark emphasised training in non-technical skills using simulation to a larger extent than the two other university campuses. This study aimed to compare the NTS performance of medical students in their last year of education at three different training sites of the same university. METHODS: This blinded cohort study included students from the three training sites who participated in identical multi-professional simulations over a six-year period. Eight raters evaluated the video recordings of eight students from each training site using the Norwegian Medical Students Non-Technical Skills (NorMS-NTS) tool. The NorMS-NTS tool, which comprises four categories and 13 elements, assesses the NTS of Norwegian medical students and assigns an overall global score. Pairwise significant differences in the NTS performance levels between the training sites studied were assessed using Tukey's test. RESULTS: The overall NTS performance levels of the medical students from Finnmark (mean 4.5) were significantly higher than those of the students from Tromsø (mean 3.8) and Bodø (mean 3.5). Similarly, the NTS performance levels at category-level of the students in Finnmark were significantly higher than those of the students from Bodø and Tromsø. Except for one category, no significant differences were observed between the students from Bodø and Tromsø in terms of the overall or category-level NTS performance. CONCLUSION: The NTS performance levels of the medical students from Finnmark, which implements rural, decentralized medical education, were significantly higher than those of the students from Tromsø and Bodø.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".