Introduction of Unresponsive Patient Simulation Scenarios Into an Undergraduate Nursing Health Assessment Course
Bibliographic record
Abstract
BACKGROUND: Despite certification in basic life support, nursing students may not be proficient in performing critical assessments and interventions for unresponsive patients. Thus, a new simulation module comprising four unresponsive patient scenarios was introduced into a second-year nursing health assessment course. METHOD: This cross-sectional study describes nursing student experience, knowledge, confidence, and performance of assessments and interventions for the unresponsive patient across 3 years of an undergraduate nursing program. RESULTS: Overall knowledge, confidence, and performance scores were similar between second-, third-, and fourth-year students (N = 239); however, performance times for many critical assessments and interventions were poor. Second-year nursing students' knowledge increased significantly following the new simulation module (p = 0.002). CONCLUSION: Findings suggest a need for more repetition of basic unresponsive patient scenarios to provide mastery. It is anticipated that addition of unresponsive patient scenarios into the second year will enhance performance by the final year of the program.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".