The effect of a repeat septic shock simulation on the knowledge and skill performance of undergraduate nursing students
Bibliographic record
Abstract
Background: Prelicensure nursing students possess minimal knowledge and skill to implement sepsis protocols effectively. This article evaluates an educational project to assess the impact of a repeat septic shock simulation on pre-licensure nursing students' knowledge and skill competency. Methods: A quasi-experimental, repeated measures, pre-post design strategy was used to evaluate a repeat septic shock simulation. A convenience sample of one-hundred-forty-three (N = 143) senior baccalaureate nursing students enrolled in the study. The project consisted of a septic shock didactic session, septic shock simulation with a high-fidelity mannequin, debrief, repeat simulation followed by a second debrief as a component of a complex health nursing course. Ninety-seven (n = 97) participants were randomly assigned to groups of up to five students to participate in a repeat septic shock simulation. Forty-six (n = 46) participants were randomly assigned to up to five students and served as a control group. The control group participated in all study elements except the repeat simulation.Results: The percent change in nursing students’ knowledge scores from Pre-simulation to Post-simulation was statistically significant (p < .001). The initial and repeat simulation realized modest gains in competency scores between the initial and repeated simulation.Conclusions: Providing concurrent experiences using a screening tool in real-time while simultaneously providing an opportunity to practice and refine clinical judgment through a repeat simulation proved effective.
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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".