Board 337 - Research Abstract Simulation with Standardized Patients in Healthcare
Bibliographic record
Abstract
Introduction/Background Simulation is a relatively new teaching tool used to help healthcare students practice various assessments and skills in a safe environment.1–3 This innovative method can develop the process of care and, thus, improve patient outcome and safety.4 In research, simulation overcomes some ethical and methodological issues, in addition to allowing control over confounding variables.5 Therefore, it is important to develop standardized clinical simulations in order to facilitate education, clinical practice and research in nursing. At the development stage, particular attention should be given to achieve good reliability and validity throughout the evaluations.6–8 A question emerges: How can we improve the reliability and validity of simulations with standardized patients? An exhaustive literature review of various methodologies of simulations with standardized patients is presented. Methods A literature review was conducted in CINAHL, MEDLINE and PubMed to examine the available research findings related to methodologies used for conducting standardized simulations. The main keywords used to search these databases were “simulation,” “validity,” “reliability,” “clinical,” “standardized” and “development.” The literature review screened for papers in English or French written between 1993 and 2013. Articles were selected for their relevancy to the subject. Results Over 500 article titles were reviewed and among them, close to 100 abstracts have been read. Overall, 56 papers were included in the literature review. Some simple Methods help enhance the reliability and validity at each step of the process, from developing a new scenario to using the finished standardized simulation with participants. There are four main elements on which it is possible to act: the creation of the scenario, the standardized patient, the flow of the simulation and the simulation environment. The scenario must be precise and every eventuality should be considered with a different path to follow. It is important to select a realistic situation customized to the participants’ learning needs and level. An expert committee including simulation and clinical setting experts increases the validity of the scenario. The selection of a standardized patient is really important for reliability and validity. He or she should receive training or be familiar with the clinical setting to be able to reproduce the same scenario with precision. If possible, the same actor should be used with all participants to maintain a level of consistency over several sessions. The standardized patient should be able to review the scenario before simulation sessions. There should be a briefing before each session, including a tour of the simulation room. The presence of a facilitator in the room ensures rigorous monitoring of the simulation flow. The simulation room should reproduce reality as faithfully as possible, with attention to details to ensure the validity of the simulation. It must feel like a safe but real environment to the participants for them to perform as they would in a real clinical setting. Finally, a pretest with a sufficient number of participants allows to experiment each of these elements and to make adjustments if necessary. Conclusion Simulations with standardized patients are a useful teaching, practice and research tool. Paying attention to the psychometric properties of the simulation helps make them more lifelike and facilitates the transfer of new practical knowledge. Some simple Methods can be put forward to promote a good reliability and validity by controlling the development of the scenario, the standardized patient, the flow of the simulation, and the simulation environment. References 1. Bambini D, Washburn J, Perkins R: Outcomes of Clinical Simulation for Novice Nursing Students: Communication, Confidence, Clinical Judgement. Nursing Education Research 2009; 30:79–82. 2. Mavis BE, Ogle KS, Lovell KL, Madden LM: Medical students as standardized patients to assess interviewing skills for pain evaluation. Medical Education 2002; 36:135–140. 3. Murray D, Boulet J, Ziv A, Woodhouse J, Kras J, McAllister J: An acute care skills evaluation for graduating medical students: a pilot study using clinical simulation. Medical Education 2002; 36:833–841. 4. Griswold S, Ponnuru S, Nishisaki A, Szyld D, Davenport M, Deutsh ES, Nadkami V: The Emerging Role of Simulation Education to Achieve Patient Safety. Translating Deliberate Practice and Debriefing to Save Lives. Pediatric Clinics of North America 2012; 59:1329–1340. 5. Gosselin E, Bourgault P, Lavoie S, Coleman RM, Méziat-Burdin A: Development and validation of an observation tool for the assessment of nursing pain management practices in intensive care unit in a standardized clinical simulation setting. Pain Management Nursing [in press]. 6. Grady JL, Rosemary GK, Trusty CE, Entin EB, Entin EE, Brunye TT: Learning Nursing Procedures: The Influence of Simulator Fidelity and Student Gender on Teaching Effectiveness. Journal of Nursing Education 2008; 47:403–8. 7. Howley L, Szauter K, Perkowski L, Clifton M, McNaughton N: Quality of standardized patient research reports in the medical education literature review and recommendations. Medical Education 2008; 42:350–8. 8. Issenberg SB, McGaghie WC, Petrusa ER, Lee Gordon D, Scalese RJ: Features and uses of high-fidelity medical simulation that lead to effective learning: a BEME systematic review. Medical teacher 2005; 27:10–28. Disclosures None.
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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 | 0.033 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.095 | 0.016 |
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".