The time is right for Web-based clinical simulation in nursing education
Bibliographic record
Abstract
E-simulation involves goal-based role play using digital simulations that take place via a computer screen. Learners interact with the program via multi-media applications such as animation and video, graphics, sound, vision, and text through the use of advanced Web authoring tools. When the simulation is Web-based (via a remote server), this allows data collection and real-time feedback. We aimed to explore how the Internet has been utilized for the purposes of e-simulation in healthcare education. We describe published resources focusing on pre-registration education for undergraduate nursing and medicine students. Many studies in these domains have developed e-simulation as components of research, but we identified only four that were openly accessible (without fees). We describe four Web-based simulation programs that will benefit learners through better understanding of cardiopulmonary resuscitation; patient deterioration recognition and management; communication with the mentally ill; and knowledge of cultural competence. These programs have the advantage of being available across borders and are accessible to a broad audience wherever there is adequate Internet access. The approach appears to be highly acceptable to learners and offers the opportunity for repeated practice. The time is right for greater distribution and sharing of Web-based simulation resources for teaching in both undergraduate and at professional levels. Web-based simulation programs are a valuable resource that can be used in combination with traditional forms of laboratory and classroom teaching, in order to facilitate the development of students’ clinical competence.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.089 | 0.048 |
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".