Medium and High Fidelity Simulations in Training Undergraduate Nursing and Operating Room Students. Are They Effective?
Bibliographic record
Abstract
Abstract Background: Simulators-Based Education is any type of educational activity that uses simulation of clinical scenarios to increase students’ awareness and skills. Simulation environments are able to create a place for students to expand their competencies in specialized skills without posing a risk to patients. In simulation, an attempt is made to bring the learning conditions so close to the real conditions, so that the learned concepts can be transferred to the real world.Methods: This is a quasi-experimental study. The statistical populations of the study were nursing and operating room (OR) students of Larestan University of Medical Sciences who were selected using census method. A total of 192 students participated in this study, which 96 students were in the control group and 96 students were in the experimental group. At the beginning of the study, students of both groups participated in the pre-test. In this study low-fidelity simulation was used to train control group, and in the experimental group training, medium- and high-fidelity simulation were applied. The post-test was performed after completion of the training program of both groups. 4 instruments were used for data collection. The data were analyzed using SPSS software (version 20.0). Results: The results of this study showed that students in the experimental group scored significantly higher in knowledge domain compare to control group students, and also performed better in the objective structured clinical examination (OSCE) (P<0.001). Also, this study showed that the average total score of students’ attitudes towards the role of simulators in the experimental group is higher than the control group. Statistical relationship was significant in some factors such as “patients’ safety (P= 0.014)”, “increasing the student's self-confidence (P= 0.041)”, “connecting the topics taught in the theoretical units (P= 0.024)”. Also, statistical relationship was meaningful in training with low-fidelity models which stated “it was not a reliable learning experience (P= 0.004)”.Conclusions: Simulation is an effective method for better teaching-learning that can increase knowledge, improve clinical competence, feel more confident and integrate knowledge in a safe and controlled environment.
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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 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".