Emergency crisis resource management: a simulation-based course developed by the Spanish Society of Emergency Medicine (SEMES) for health sciences students
Bibliographic record
Abstract
OBJECTIVES: Educational programs based on high-fidelity simulation training aim to promote students' acquisition of nontechnical competencies such as understanding crisis resource management (CRM). This study evaluated the efficacy of a CRM course for students in their last year of university studies in health sciences. The course was developed by the Spanish Society of Emergency Medicine (SEMES). MATERIAL AND METHODS: Quasi-experimental study of a high-fidelity simulation course to teach emergency CRM (E-CRM) using preand postcourse measures of achievement in a single student cohort. A total of 209 students completed 2 selfadministered self-efficacy evaluations of their acquisition of nontechnical competencies and resilience. External observers also assessed the students' nontechnical competencies with objective measurement scales. RESULTS: Scores on resilience and self-efficacy assessments improved through the intervention (F = 25.90 and F = 68.02, respectively; P .001, for both pre-post comparisons). Statistically significant differences were found between students in different health sciences at baseline (t = 2.67; P = .008). Scores improved significantly on the Mayo High Performance Teamwork Scale (F = 6.18, P .001, eta2 = 0.20) and the Ottawa CRM Global Rating Scale (F = 5.58; P .005, eta2 = 0.19). CONCLUSION: The E-CRM course developed by a coordinated multiprofessional team based on high-fidelity simulations improved self-efficacy assessments of resilience and all nontechnical competencies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".