Ebola Virus Hemorrhagic Fever: A Simulation-Based Clinical Education Experience Designed for Senior Undergraduate Nursing Students
Bibliographic record
Abstract
In light of the recent Ebola virus disease outbreak, the nursing faculty at Memorial University of Newfoundland, School of Nursing recognized the need to reassess and reinforce undergraduate nursing students’ knowledge and skills related to infection prevention and control precautions and the use of personal protective equipment (PPE). Senior nursing students may have a very limited role in the identification of an Ebola case, depending on clinical placement settings, but teaching them about the expanded precautions used in the care of Ebola cases can serve to reinforce understanding of principles, stimulate interest in infection control, and enhance technical skills that are transferable to other patients with infections. A simulation-based clinical education experience designed for senior students in their final year of studies in the Bachelor of Nursing (Collaborative) Program was developed using a step-wise approach and following the deteriorating patient scenario (DPS) method. The simulation has four implementation frames that are linked to the four key learning objectives, which include the following: (1) recognize Ebola virus disease compatible symptoms, (2) implement the guidelines for expanded isolation precautions to prevent the transmission of the Ebola virus, (3) demonstrate the proper sequence for donning and doffing personal protective equipment (PPE), and (4) remove risk for exposure to the Ebola virus through decontamination. The simulation experience concludes with a video-based debriefing that followed a modified rapid cycle deliberate practice method.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".