Board 182 - Program Innovations Abstract Development And Evaluation Of Simulation-Based Fever Management Module For Children With Febrile Convulsion (Submission #479)
Bibliographic record
Abstract
Introduction/Background Simulation-based education gives students an opportunity to lessen the gap between academic and clinical settings, learn core nursing skills and enhance self-confidence through learning. Student performance evaluation is a very important step during simulation-based practice.23 Although a module may be well developed, a standardized evaluation checklist is essential for confirming the effectiveness of simulation-based practice. To enhance the effectiveness of simulations in nursing education, a reliable and valid evaluation instrument is needed to measure student performance. Furthermore, a valid instrument is required for the simulation to be considered a formalized implementation and evaluation tool. Fever is a common symptom found in children with disease in pediatric care units. Fever in children has been acknowledged as a common occurrence in pediatric nursing. Students may frequently encounter children with febrile convulsions in a clinical setting. Simulation modules may provide a comprehensive understanding of fever and fever management. However, there is no evaluation checklist to measure systematically simulation-based performance of students dealing with febrile convulsions. Furthermore, students satisfaction with the simulation is important for meaningful learning and facilitating active participation.22 Therefore, a simulation-based fever management module for children with febrile convulsions is needed, since this is one of the most frequent problems experienced by a student in a clinical nursing setting. This study focuses on pediatric nursing education and nursing practice. The following research issues were addressed: a) to develop a simulation-based fever management module for pediatric febrile convulsions and b) to evaluate students performances and satisfaction. Methods The development of the simulation-based fever management module and evaluation checklist for treating children with febrile convulsions was a three-stage process. Stage I: developing the simulation-based module; Stage II: developing programs for nursing students; and Stage III: evaluating the simulation-based module and validating the dimensions.The module included an algorithm with the scenario, evaluation checklist and debriefing plan. For scenario development, we collected information on nursing care from child health nursing textbooks and nursing journals. This scenario was based on a real febrile convulsion case that had occurred in a general hospital. The evaluation checklist focused on the attainment of nursing goals based on nursing processes rather than outcomes. The categories were divided into assessment, problem identification, intervention and evaluation. The evaluation checklist score was based on a 4-point Likert scale. The higher the evaluation checklist score, the higher the performance rating. The researchers developed the following debriefing questions: a) What did you learn?; b) How did you feel?; c) What did you do?; d) What were your strong and weak points? Debriefing was conducted at the end of the study and took about 20-30 minutes per group of 3-4 students. Students’ satisfaction with the clinical simulation was measured using the Satisfaction of Simulations Experience [SSE] scale developed by Levett-Jones and colleagues (2011). This scale consists of 18 items: debrief and reflection (9 items), clinical reasoning (5 items), and clinical learning (4 items). Each item was scored on a 5-point Likert scale. Higher scores indicated higher satisfaction. The target population for this study included undergraduate students. One hundred forty-seven nursing students were selected from two universities located in Seoul, South Korea. Data were collected from April 29, 2013 to June 14, 2013. Each simulation lasted 20-30 minutes, with both the simulation and simulation class taking about two hours per group. In addition, an SSE was used to measure student satisfaction with the simulation. Collected data were analyzed using SPSS 18.0 for Windows to calculate descriptive statistics for the evaluation checklist and the SSE. Debriefing data were analyzed using the Matrix Method. A scenario script was formulated to treat the patient’s health issues. The algorithm proceeded as follows: identification of patients’ condition (Step I), nursing intervention (Step II), and outcome evaluation and feedback (Step III). To test the internal consistency and reliability of the evaluation checklist and each subscale, Cronbach’s alpha coefficient was measured. Cronbach’s alpha for the evaluation checklist was .865; each subcategory ranged from .706 to .808. The evaluation checklist consisted of four categories based on nursing processes: assessment, problem identification, intervention and evaluations. The total mean score of the evaluation checklist was 2.67 (± .32). The mean score of each category was as follows: assessment, 2.35 (± .37); problem identification, 2.94 (± .46); intervention, 2.89 (± .40); and evaluation, 2.82 (± .56). The categories were as follows: non-technical skill (30.7%), self-efficacy (29.8%), critical thinking (21.8%), and technical skill (17.8%). The most frequent categories, in order, were teamwork and collaboration (n = 27), reflection (n = 26), situation recognition (n = 21), core nursing skills (n = 21), emotional support (n = 21), and therapeutic communication (n = 21). The total mean score of SSE was 4.48 (± .42). The mean score for debrief and reflection was 4.55 (± .43), clinical reasoning was 4.39 (± .50), and clinical learning was 4.43 (±.48). Results: Conclusion This study was able to develop more reliable algorithms and evaluation tools for fever management in nursing care. This study identified a nursing care module for children with febrile convulsions. The implications for nursing education include determining how nursing students care for patients using critical thinking and nursing skills. This study also provides a blueprint for simulation-based practice for both nursing educators and nursing students. Furthermore, this study recommends developing an evaluation checklist for other nursing education programs. A large sample size in more than one geographic location will provide valid and reliable data for this module and the evaluation checklist. In addition, this study highlights the need for the full integration of simulations into nursing curricula. References 1. Abdo, A., Ravert, P. 2006. Student satisfaction with simulation experiences. Clinical Simulation in Nursing Education, 2, e13-e16. 2. Berragan, L. 2011. Simulation: an effective pedagogical approach for nursing? Nurse Education Today, 31, 660-663. 3. Bland, A. J., Topping, A., Wood, B. 2011. A concept analysis of simulation as a learning strategy in the education of undergraduate nursing students. Nurse Education Today, 31, 664-670. 4. Bremner, M., Aduddell, K., Bennett, F., VanGeest, J. 2006. The use of human patient simulators: best practice with novice nursing students. Nurse Educator, 31 (4), 170-174. 5. Cant, R. P., Cooper, S. J. 2010. Simulation-based learning in nurse education: systematic review. Journal of Advanced Nursing, 66 (1), 3-15. 6. Cioffi, J. 2001. Clinical simulations: Development and validation. Nurse Education Today, 21, 477-486. 7. Costello, M. 2011. The use of simulation in medication calculation instruction: a pilot study. Nurse Educator, 36 (5), 181-182. 8. Garrad, J. 2007. Health Sciences Literature Review Made Easy: The Matrix Method. 2nd ed. Jones and Bartlett, Sudbury, MA. 9. Gillan, P. C., Parmenter, G., Riet, P. J., Jeong, S. 2012. The experience of end of life care simulation at rural Australian university. Nurse Education Today, 32, 319-329. 10. Hockenberry, M. J. 2005. Wong’s Essentials of Pediatric Nursing. 7th ed. Elsevier Mosby, St. Louis, MO. 11. Jeffries, P. R. 2007. Simulation in Nursing Education: From Conceptualization to Evaluation. National League of Nursing, NY. 12. Jeffries, P. R., Norton, B. 2005. Selecting Learning Experiences to Achieve Curriculum Outcomes. In D. M. Billings & J. A. Halstead (eds.), Teaching in Nursing: A Guide for Faculty (2nd ed., pp. 187-212). Elsevier, MO. 13. Potts, N. L., Mandleco, B. L. 2011. Pediatric Nursing: Caring for Children and Their Families. 3rd ed. Delma Cengage Learning; NY. 14. Levett-Jones, T., McCoy, M., Lapkin, S., Noble, D., Hoffman, K., Dempsey, J., Arthur, C., Roche, J. 2011. The development and psychometric testing of the satisfaction with simulation experience scale. Nurse Education Today, 31, 705-710. 15. Lichtman, M. 2006. Qualitative Research in Education: A User’s Guide. Sage, Thousand Oaks, CA. 16. Lynn, M. R. 1986. Determination and quantification of content validity. Nursing Research, 35 (6), 382-385. 17. Moule, P. 2011. Simulation in nurse education: Past, present and future. Nurse Education Today, 31, 645-646. 18. Murphy, J. I. 2013. Using plan do study act to transform a simulation center. Clinical Simulation in Nursing, 9 (7), e257-e264. 19. Nehring, W. M., Ellis, W. E., Lashley, F. R. 2001. Human patient simulators in nursing education: an overview. Simulation & Gaming, 32, 194-204. 20. Oh, J., Kang, J., De Gagne, J. C. 2012. Learning concepts of cinenurducation: an integrative review. Nurse Education Today, 32, 914-919. 21. Poirier, M., Davis, P., Gonzalez-Del Rey, J., Monroe, K. 2000. Pediatric emergency department nurses’ perspectives on fever in children. Pediatric Emergency Care, 16 (1), 9-12. 22. Prion, S. 2008. A practical framework for evaluating the impact of clinical simulation experience in prelicensure nursing education. Clinical Simulation in Nursing, 4, e69-e78. 23. Reed, S. J. 2010. Designing a simulation for student evaluation using Scriven’s key evaluation checklist. Clinical Simulation in Nursing, 6 (2), e41-e44. 24. Roh, Y. S., Lee, W. S., Chung, H. S., Park, Y. M. 2013. The effects of simulation-based resuscitation training on nurses’ self-efficacy and satisfaction. Nurse Education Today, 33, 123-128. 25. Sarrell, M., Cohen, H., Kahan, E. 2002. Physicians’, nurses’, and parents’ attitudes to and knowledge about fever in early childhood. Patient Education and Counselling, 46, 61-65. 26. Schoening, A., Sittner, B., Todd, M. 2006. Simulated clinical experience: nursing students’ perceptions and the educator’s role. Nurse Educator, 31 (6), 253-258. 27. Walsh, A. M., Edwards, H. E., Courtney, M. D., Wilson, J. E., Monaghan, S. J. 2006. Paediatric fever management: continuing education for clinical nurses. Nurse Education Today, 26, 71-77. 28. Walts, C. F., Bausell, R. B. 1981. Nursing: Design, Statistics & Computer Analysis. F.A. Davis Co., Philadelphia, NY. 29. Wolf, L., Dion, K., Lamourezux, E., Kenny, C., Curnin, M., Hogan, M. A., Roche, J., Cunningham, H. 2011. Using simulated clinical scenarios to evaluate student performance. Nurse Educator, 36(3), 128-134. 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.135 | 0.024 |
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".