The impact of low-cost, optimal-fidelity simulation on physical therapy students’ clinical performance and self-efficacy: a pilot study
Main Article Content
Abstract
Purpose: Patient simulation has emerged as a useful tool to refine cognitive, psychomotor, and affective skills in realistic yet controlled settings. However, the cost associated with simulation labs can be a barrier. The purpose of this pilot study was to 1) assess the feasibility of a low-cost optimal-fidelity simulation lab integrated into an academic course and 2) assess the effectiveness of a low-cost optimal-fidelity patient simulation on self-efficacy and clinical performance of Doctor of Physical Therapy (DPT) students.
Methods: This prospective study utilized a repeated measures quasi-experimental research design. Subjects were recruited through convenience sampling from two branches of the same accredited program run separately on different campuses. Students from one campus served as the experimental group and students from the other campus served as the control group. The control group received usual training for a course on patient assessment. Simultaneously, the experimental group received usual training with the addition of simulation. The Jones and Sheppard self-efficacy questionnaire was administered at baseline (T0), after simulation (T1), and after the subjects’ first clinical experience (T2). The PT Clinical Performance Instrument (CPI) assessed clinical performance. Faculty time, space, equipment, and funds were recorded for descriptive analysis
Results: A low-cost optimal-fidelity simulation lab was developed in a 360 square feet room with approximately $500 of supplies. Mann–Whitney independent sample tests demonstrated no statistical significance between groups at each of the data collection points. Within group changes in self-efficacy were statistically significant from T0 to T1 in the experimental group only. No statistically significant changes in CPI scores were noted between groups at the midterm or final assessment. A small-to-moderate effect size (d = 0.386) was noted.
Conclusion: The feasibility of the low-cost optimal-fidelity simulation was demonstrated by the limited cost and space requirements. Exposure to one simulated patient encounter appeared to accelerate the development of self-efficacy prior to a first clinical placement compared to usual training in this pilot study.
References
Gonzalo JD, Haidet P, Papp KK, et al. Educating for the 21st-century health care system: an interdependent framework of basic, clinical, and systems sciences. Acad Med (2017) 92(1): 35–9. doi: 10.1097/ACM.0000000000000951
Kellett J, Papageorgiou A, Cavenagh P, et al. The preparedness of newly qualified doctors – views of foundation doctors and supervisors. Med Teach (2015) 37(10): 949–54. doi: 10.3109/0142159X.2014.970619
Maran NJ, Glavin RJ. Low- to high-fidelity simulation – A continuum of medical education? Med Educ (2003) 37(Suppl 1): 22–8. doi: 10.1046/j.1365-2923.37.s1.9.x
Motola I, Devine LA, Chung HS, et al. Simulation in healthcare education: a best evidence practical guide. AMEE guide no. 82. Med Teach (2013) 35(10): 1511. doi: 10.3109/0142159X.2013.818632
Stockert B, Ohtake PJ. A national survey on the use of immersive simulation for interprofessional education in physical therapist education programs. Simul Health (2017) 12(5): 298–303. doi: 10.1097/SIH.0000000000000231
Cook DA, Hatala R, Brydges R, et al. Technology-enhanced simulation for health professions education: a systematic review and meta-analysis. JAMA (2011) 306(9): 978–88. doi: 10.1001/jama.2011.1234
Norman J. Systematic review of the literature on simulation in nursing education. ABNF J (2012) 23(2): 24–8.
Sabus C, Macauley K. Simulation in physical therapy education and practice: opportunities and evidence-based instruction to achieve meaningful learning outcomes. J Phys Ther Educ (2016) 30(1): 3–13. doi: 10.1097/00001416-201630010-00002
Silberman NJ, Panzarella KJ, Melzer BA. Using human simulation to prepare physical therapy students for acute care clinical practice. J Allied Health (2013) 42(1): 25–32.
Shoemaker MJ, Riemersma L, Perkins R. Use of high fidelity human simulation to teach physical therapist decision-making skills for the intensive care setting. Cardiopulm Phys Ther J (2009) 20(1): 13–18. doi: 10.1097/01823246-200920010-00003
Laschinger S, Medves J, Pulling C, et al. Effectiveness of simulation on health profession students’ knowledge, skills, confidence and satisfaction. JBI Libr Syst Rev (2008) 6(7): 265–309. doi: 10.1111/j.1744-1609.2008.00108.x
Hamstra SJ, Brydges R, Hatala R, et al. Reconsidering fidelity in simulation-based training. Acad Med (2014) 89(3): 387–92. doi: 10.1097/ACM.0000000000000130
Massoth C, Röder H, Ohlenburg H, et al. High-fidelity is not superior to low-fidelity simulation but leads to overconfidence in medical students. BMC Med Educ (2019) 19(1): 29. doi: 10.1186/s12909-019-1464-7
Meurling L, Hedman L, Lidefelt K, et al. Comparison of high- and low equipment fidelity during paediatric simulation team training: a case control study. BMC Med Educ (2014) 14: 221. doi: 10.1186/1472-6920-14-221
Nimbalkar A, Patel D, Kungwani A, et al. Randomized control trial of high fidelity vs. low fidelity simulation for training undergraduate students in neonatal resuscitation. BMC Res Notes (2015) 8: 636. doi: 10.1186/s13104-015-1623-9
Norman G, Dore K, Grierson L. The minimal relationship between simulation fidelity and transfer of learning. Med Educ (2012) 46(7): 636–47. doi: 10.1111/j.1365-2923.2012.04243.x
Schoenherr JR, Hamstra SJ. Beyond fidelity: deconstructing the seductive simplicity of fidelity in simulator-based education in the health care professions. Simul Healthc (2017) 12(2): 117–23. doi: 10.1097/SIH.0000000000000226
Weller JM, Nestel D, Marshall SD, et al. Simulation in clinical teaching and learning. Med J Aust (2012) 196(9): 594. doi: 10.5694/mja10.11474
Park JH, Son JY, Kim S, et al. Effect of feedback from standardized patients on medical students’ performance and perceptions of the neurological examination. Med Teach (2011) 33(12): 1005–10. doi: 10.3109/0142159X.2011.588735
Paskins Z, Peile E. Final year medical students’ views on simulation-based teaching: a comparison with the best evidence medical education systematic review. Med Teach (2010) 32(7): 569–77. doi: 10.3109/01421590903544710
Opacic DA. The relationship between self-efficacy and student physician assistant clinical performance. J Allied Health (2003) 32(3): 158–66.
Bandura A. Human agency in social cognitive theory. Am Psychol (1989) 44(9): 1175–84. doi: 10.1037/0003-066x.44.9.1175
van Lankveld W, Jones A, Brunnekreef JJ, et al. Assessing physical therapist students’ self-efficacy: measurement properties of the physiotherapist self-efficacy (PSE) questionnaire. BMC Med Educ (2017) 17(1): 250. doi: 10.1186/s12909-017-1094-x
Thomas EM, Rybski MF, Apke TL, et al. An acute interprofessional simulation experience for occupational and physical therapy students: key findings from a survey study. J Interprof Care (2017) 31(3): 317–24. doi: 10.1080/13561820.2017.1280006
Jones A, Sheppard L. Self-efficacy and clinical performance: a physiotherapy example. Adv Physiother (2011) 13(2): 79–83. doi: 10.3109/14038196.2011.565072
Roach KE, Frost JS, Francis NJ, et al. Validation of the revised physical therapist clinical performance instrument (PT CPI): version 2006. Phys Ther (2012) 92(3): 416–28. doi: 10.2522/ptj.20110129
Hanberg A, Brown SC, Hoadley T, et al. Finding funding: the nurses’ guide to simulation success. Clin Simul Nurs (2007) 3(1): 5–9. doi: 10.1016/j.ecns.2009.05.032
Hough J, Levan D, Steele M, et al. Simulation-based education improves student self-efficacy in physiotherapy assessment and management of paediatric patients. BMC Med Educ (2019) 19(1): 463. doi: 10.1186/s12909-019-1894-2
Silberman N, Litwin B, Panzarella K, et al. Student clinical performance in acute care enhanced through simulation training. J Acute Care Phys Ther (2015) 1: 25–37. doi: 10.1097/JAT.0000000000000021
O’Connor A, McGarr O, Cantillon P, et al. Clinical performance assessment tools in physiotherapy practice education: a systematic review. Physiotherapy (2018) 104(1): 46–53. doi: 10.1016/j.physio.2017.01.005
Sittner BJ, Aebersold ML, Paige JB, et al. INACSL standards of best practice for simulation: past, present, and future. Nurs Educ Perspect (2015) 36(5): 294–8. doi: 10.5480/15-1670
Cooke JM, Rooney DM, Fernandez GL, et al. Simulation center best practices: a review of ACS-accredited educational institutes’ best practices, 2011 to present. Surgery (2018) 163(4): 916–20. doi: 10.1016/j.surg.2017.11.004
Calhoun AW, Nadkarni V, Venegas-Borsellino C, White ML, Kurrek M. Concepts for the simulation community: Development of the international simulation data registry. Simul Healthc (2018) 13(6): 427–34: doi: 10.1097/SIH.0000000000000311
Bonnel W, Hober C. Optimizing the reflective observer role in high-fidelity patient simulation. J Nurs Educ (2016) 55(6): 353–56. doi: 10.3928/01484834-20160516-10
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.