Standardized Patient Simulation in Healthcare Education
Bibliographic record
Abstract
Introduction: Simulation training is a key part of healthcare and medical education, but the use of standardized patients in simulation training is not yet systematic at least in Finland.The purpose of this literature review was to determine the benefits and challenges of using standardized patients in simulation training.Methods: The information search was conducted in the international Cinahl and Pubmed databases.The search terms used were "Simulation", "Simulation-based education", "Simulated patient", "Standardized patient" and "Standardized patient".The inclusion criteria were 1) peer-reviewed original research 2) published in Finnish or English 3) published in the last 10 years.Additionally, the research had to be conducted in Europe, the United States, Canada, or Australia.40 studies fulfill the criteria.The data was analyzed with inductive content analysis.Results: The use of a standardized patient in simulation training improves student learning outcomes, as well as develops interaction and work-life skills.For the standardized patient, participating in simulation training can increase acting experience and bring content to everyday life.However, it can also be physically and mentally taxing.It's important that the teacher enables good preparation for the role.Challenges experienced by students are related to stress created by the standardized patient and unexpected situations in the simulation of standardized patients.Discussion: The results show that the utilization of standardized patients in simulation requires planning and adequate resources, but it also brings various benefits, especially for the development of students' skills.
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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".