From Centralized to Decentralized Model of Simulation-Based Education: Curricular Integration of Take-Home Simulators in Nursing Education
Bibliographic record
Abstract
In a centralized model of simulation-based education (Ce-SBE), students practice skills in simulation laboratories, while in a decentralized model (De-SBE), they practice skills outside of these laboratories. The cost of "take-home" simulators is a barrier that can be overcome with additive manufacturing (AM). Our objective was to develop and evaluate the quality of education when year one nursing students practiced clinical skills from home following normal curricular activities but in the De-SBE format. A group of expert educators, designers, and researchers followed a two-cycle, iterative design-to-cost approach to develop three simulators: wound care and urethral catheterization (male and female). The total cost of manufacturing all three simulators was USD 5,000. These were sent to all year one nursing students who followed an online curriculum. Twenty-nine students completed the survey, which indicated that the simulators supported the students' learning needs, and several changes were requested to improve the educational value. The results indicate that substituting traditional simulators with AM-simulators provided an acceptable alternative for nursing students to learn wound care and urethral catheterization off-campus in De-SBE. The feedback also provided suggestions to improve each of the simulators to make the experience more authentic.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".