International Journal of Nursing Education Scholarship 2014; 11(1): 83–90
Research Article Young Sook Roh and Eun Ju Lim*
Pre-Course Simulation as a Predictor of Satisfaction with an Emergency Nursing Clinical Course Abstract: Recent research suggests that simulation education can effectively improve nursing students’ practical competence and can enhance educational outcomes. But very few studies have identified the relationships between pre-course simulation and course satisfaction. The purpose of this study was to determine whether pre-course simulations and other advanced learning modalities (i.e. pre-course e-learning, observation, and clinical placement skill performance) predicted students’ satisfaction with an emergency nursing clinical course. Second-year Korean nursing students (N ¼ 284) participated in an integrated clinical course consisting of selfdirected pre-course e-learning, a 2-hour pre-course simulation, and an 80-hour emergency room clinical placement with observation. Multiple regression analyses found that pre-course simulation, clinical placement skill performance, observation during the clinical placement, and pre-course e-learning accounted for 47.2% of the variance in course satisfaction. Notably, pre-course simulation made the largest contribution to course satisfaction, accounting for 29.1% of the variance. Pre-course simulation, skill performance, observation, and precourse e-learning all significantly influenced learner satisfaction. Findings suggest that integrating simulation into the clinical curriculum may enhance clinical course satisfaction. Keywords: simulation, course satisfaction, emergency nursing, clinical placement DOI 10.1515/ijnes-2013-0083
*Corresponding author: Eun Ju Lim, Red Cross College of Nursing, Chung-Ang University (CAU), 84 Heukseok-ro Dongjak-gu, Seoul 156-756, Korea, E-mail: [email protected] Young Sook Roh, Red Cross College of Nursing, Chung-Ang University (CAU), 84 Heukseok-ro Dongjak-gu, Seoul 156-756, Korea, E-mail: [email protected]
Clinical placement is thought to be necessary for nursing students to become competent in their profession (AlKandari, Vidal, & Thomas, 2009; Williams, French, & Brown, 2009). Nursing students have reported that clinical exposure enhanced their orientation to the field and promoted role actualization (Michalec, Diefenbeck, & Mahoney, 2013). However, one descriptive survey demonstrated that nursing students’ opportunities to practice certain critical skills, supervision, and assessment within their clinical placements were less than optimal for competent skill development (Stayt & Merriman, 2012). The development of sustainable approaches for enhancing nursing students’ clinical learning experience is an international concern (Newton, Jolly, Ockerby, & Cross, 2012). Increasing student numbers, reduced length of inpatient stay, and the reduced availability of clinical teachers have fueled the need for clinical education that involves simulation (Issenberg, McGaghie, Petrusa, Lee Gordon, & Scalese, 2005). A recent meta-analysis demonstrated that simulation-based medical education with deliberate practice is superior to traditional clinical education approaches in the achievement of specific clinical skill acquisition goals (McGaghie, Issenberg, Cohen, Barsuk, & Wayne, 2011). However, some have suggested that simulation must be used alongside, and linked closely with, clinical practice (Kneebone, Scott, Darzi, & Horrocks, 2004; McGaghie, Siddall, Mazmanian, & Myers, 2009; Meyer, Connors, Hou, & Gajewski, 2011). Regretfully, evidence in support of the idea that simulation is a valuable supplement to clinical placement is sparse. The measurement of a learner’s course satisfaction can convince stakeholder groups to support specific learning modalities (Kirkpatrick & Kirkpatrick, 2006). However, the reported positive outcomes of simulation or other learning modalities used in clinical education do not dilute concerns about whether these positive outcomes directly lead to improved satisfaction with a clinical course. Thus, further study is required to afford
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Y. S. Roh and E. J. Lim: Pre-Course Simulation as a Predictor of Satisfaction
evidence that this blended learning approach is a viable supplement to traditional learning methods.
Literature review Currently, there is much discussion worldwide regarding the potential for simulation to replace some of the required clinical placement hours (Arthur, Kable, & Levett-Jones, 2011). One study has provided evidence that clinical education in simulated learning environments can result in a 25% reduction in clinical time with real patients without compromising the students’ attainment of necessary professional competencies (Watson et al., 2012). A recent meta-analysis has demonstrated that simulation-based medical education with deliberate practice is superior to traditional clinical medical education in the achievement of specific clinical skill acquisition goals (McGaghie et al., 2011). However, some educators suggest that simulation must be used alongside, and linked closely with, clinical practice (Kneebone et al., 2004; Meyer et al., 2011). Others argue that medical simulation can be used only to complement, not replace, educational activities based on real patient care experiences (McGaghie et al., 2009). A focus group interview with key midwifery academics has suggested that simulation could be unrealistic (McKenna et al., 2011). Despite an increase in the use of simulation with positive educational outcomes in nursing education (Rochester et al., 2012), very few studies have found associations between pre-placement simulation and clinical placement (Khalaila, 2014; Williams et al., 2009). The potential of e-learning strategies also has been considered along with simulation, as e-strategies afford wider access and availability than “classical” learning techniques (Perkins et al., 2010). A meta-analysis by Cook et al. (2010) found that e-learning increased students’ control of the content, place, and time of learning. Other education-related studies have demonstrated that pre-course preparatory strategies, including computerassisted learning tutorials, enhance knowledge acquisition and reduce classroom time (Bhanji et al., 2010). Over 70% of healthcare providers reported that pre-course elearning improved their understanding of an Advanced Life Support course’s key learning domains (Perkins et al., 2010). A study of digital video disk simulations found that nursing students perceived that this approach helped to familiarize them with clinical placements (Williams et al., 2009). However, one systematic review of the benefits of e-learning for nurses and student nurses
afforded no results on these individuals’ satisfaction with their e-learning experiences, because the satisfaction data from three studies were not available (Lahti, Hätönen, & Välimäki, 2013). In general, a literature review has indicated that the judicious use of a skills laboratory, consistent clinical placement, supportive clinical learning environment, and effective coaching by clinical educators positively affected student outcomes (Tanda & Denham, 2009). In addition, Brown et al. (2011) reported that task orientation, student involvement, personalization, and innovation were significant predictors of health science students’ self-reported satisfaction with clinical learning environments. As well, effective clinical learning is believed to require integrating nursing students into ward activities, engaging staff in the process of addressing individual student learning needs, supportive clinical learning activities, and implementing innovative teaching approaches (Henderson, Cooke, Creedy, & Walker, 2012). Despite the fact that innovative teaching approaches, including pre-course simulation and e-learning, increasingly have been adopted in order to foster student-centered clinical learning, very few studies have found associations between pre-placement simulation and clinical placement (Khalaila, 2014; Williams et al., 2009). Learner satisfaction and its relationships with innovative teaching approaches and clinical placement are not yet fully understood. Further research is needed to inform evidence-based teaching approaches that enhance nursing students’ clinical learning experiences. Accordingly, the purpose of this study was to determine whether precourse simulation and other learning modalities (specifically, pre-course e-learning, observation, and clinical placement skill performance) predicted student satisfaction with an emergency nursing clinical course.
Methods Design A cross-sectional descriptive survey design employing regression analysis was selected to advance knowledge from previous research by exploring the relationships between pre-course simulation, e-learning, observation, clinical placement skill performance, and student satisfaction with an emergency nursing clinical course. The emergency nursing clinical course consisted of a self-directed pre-course e-learning program presenting an orientation to emergency nursing (i.e. environment, patient
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Y. S. Roh and E. J. Lim: Pre-Course Simulation as a Predictor of Satisfaction
management, equipment, medication, resuscitation, and medical, surgical, and toxicological emergencies), a 2-hour pre-course simulation providing cardiopulmonary resuscitation (CPR) skills training, and an 80-hour clinical placement in an emergency department.
Participants A sample size calculation using G*power 3.0.10 software (Faul, Erdfelder, Buchner, & Lang, 2009) indicated that with four predictors in a stepwise multiple regression analysis, a minimum sample size of 129 is required to obtain a moderate effect size of 0.15, with a significance of 0.05 and a power of 0.95, using F-tests. To meet this requirement, a convenience sample of 290 second-year students enrolled in an emergency nursing clinical course was approached and recruited for participation, regardless of age or gender, at the time of their clinical rotation (recruitment rate ¼ 100%). The participants thus came from 14 groups of 20–24 students rotated every 2 weeks through an emergency department for clinical placement. Six incomplete questionnaires were excluded from the analyses; thus, the data from 284 nursing students (participation rate ¼ 98%) were analyzed.
Procedure Approval of research ethics was granted by the Ethics Committee of the college of nursing. All participants signed an informed consent form before entering the study, which took place from September 2011 to June 2012. The pre-course simulation of 2 hours of CPR skills training was conducted 1 week prior to the student’s emergency department rotation. The same instructor participated in all pre-course simulation sessions to ensure that the lecture and training style was consistent for all participants. All participants studied the principles of and the algorithm for adult basic life support (BLS) before their pre-course simulation session. They then received a 30-minute didactic lecture about the Korea National Adult BLS Guidelines, as well as 1 hour of CPR skills training that included hands-on practice using videomediated, practice-while-watching instruction. The CPR skills training was conducted using a static manikin, with only a torso and a head, which was placed in a hospital bed. After the training, all participants tested their CPR skills on sensor-equipped adult manikins (Resusci Anne™; Laerdal Medical, Stavanger, Norway). At the end of the pre-course simulation session, the instructor
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provided each student with feedback based on performance data printed from the SkillReporter. This feedback allowed the students to reflect on and analyze their behavior. One week after this pre-course simulation experience, sub-groups of two to six of the 20–24 nursing students in each clinical rotation were assigned to 8hour long day or evening shifts for 2 weeks in each of two semesters at one of seven different emergency departments. These seven emergency departments had 22–46 patient beds. Three faculty members from the college of nursing provided the 4 hours of clinical teaching per week, with each faculty member teaching in two or three of the seven emergency departments on either the day or evening shift. During the first week of this clinical experience, the student participants used an electronic medical record system to record the progress of their assigned patients, and the faculty provided them with feedback regarding their clinical experiences. Faculty demonstrated how to use the emergency department equipment and crash cart, and the students practiced resuscitation skills (e.g. defibrillation and assisting with intubation) under faculty supervision. Student participants’ clinical skills and behaviors in the emergency department were assessed using the clinical performance checklists developed by the authors. During the second week in the emergency department, students presented their clinical cases and received feedback from their peers and faculty during clinical teaching sessions held in a conference room. The clinical performance checklists were reviewed to promote reflection on the students’ clinical experiences. All participants filled out self-administered instruments after completing the clinical course.
Measures Course satisfaction was measured using the Satisfaction with Clinical Course Scale (Lee, Kim, & Kim, 2004). This instrument consists of 30 items assessing six factors (Faculty, Environment, Time, Content, Preceptor, and Evaluation) rated on 5-point Likert-type scales (1 ¼ very dissatisfied; 5 ¼ very satisfied). Higher mean scores indicate higher satisfaction. In a previous study, the instrument had a Cronbach’s α value of 0.87 (Lee et al., 2004). The Cronbach’s α value in this study was 0.92. Satisfaction with pre-course simulation was measured using the Satisfaction with Simulation Experience Scale (Levett-Jones et al., 2011). This 18-item instrument
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Y. S. Roh and E. J. Lim: Pre-Course Simulation as a Predictor of Satisfaction
assesses satisfaction with three aspects of simulation, namely, Debriefing and Reflection, Clinical Reasoning, and Clinical Learning, on 5-point Likert-type scales (1 ¼ very dissatisfied; 5 ¼ very satisfied). Higher mean scores indicate higher satisfaction. In a previous study, this instrument had a Cronbach’s α value of 0.77 (LevettJones et al., 2011). The Cronbach’s α in the study reported herein was 0.94. Satisfaction with the learning modality was measured by asking the participants to report their level of satisfaction for three learning modalities, pre-course e-learning, observation, and clinical placement skill performance, rating each on a 5-point Likert-type scale (1 ¼ very dissatisfied; 5 ¼ very satisfied). Higher mean scores indicate higher satisfaction.
the strongest bivariate correlation with the dependent variable. The second variable to enter the equation was the one that produced the largest increase to R2 when used simultaneously with the variable selected in the first step. This procedure continued until no additional predictor significantly increased the value of R2 (Polit & Beck, 2011). The significance threshold was set at 0.05 for all tests. There were no missing data in the responses of the 284 student participants ultimately in the study.
Results Demographic characteristics of the nursing students
Data analysis All statistical analyses were performed using SPSS version 20.0 (SPSS Inc., Chicago, IL). Descriptive statistics were calculated for the general characteristics of the participants and the study variables. Pearson correlation coefficients were used to assess the relationships between course satisfaction and the other study variables. Stepwise multiple regression analyses were employed to determine whether any of the four learning modalities (i.e. pre-course simulation, pre-course e-learning, observation, and clinical placement skill performance) predicted satisfaction with an emergency nursing clinical course. For the stepwise multiple regression, predictors were entered into the regression equation in sequential order from the highest to the lowest influences on R2. The first step was to select the single best predictor of the dependent variable, that is, the independent variable with
Table 1
Of the 284 participants, 91% (N ¼ 259) were women. Their ages ranged from 18 to 49 years, with a mean age of 21.7 years (SD ¼ 4.48). Prior to the study, none of the participating students had any experience working with sensorequipped adult manikins.
Descriptive statistics The descriptive statistics for the study variables are presented in Table 1. On a 5-point scale, the overall mean course satisfaction score was 3.56 (SD ¼ 0.55). Students gave the highest scores to the factor “faculty” (mean ¼ 4.04, SD ¼ 0.69) and the lowest to “evaluation” (mean ¼ 3.12, SD ¼ 0.84). On a 5-point scale, the overall mean score was 4.36 (SD ¼ 0.80) for observation during the clinical placement, 4.18 (SD ¼ 0.50) for satisfaction with
Descriptive statistics for the study variables (N ¼ 284)
Variable Course satisfaction Faculty Environment Time Content Preceptor Evaluation Satisfaction with pre-course simulation Satisfaction with learning modality Observation during the clinical placement Pre-course e-learning Clinical placement skill performance
Mean SD
Range
Cronbach’s α
3.56 0.55 4.04 0.69 3.96 0.60 3.40 0.88 3.39 0.60 3.25 1.05 3.12 0.84 4.18 0.50
1.90–5.00 1.67–5.00 2.13–5.00 1.00–5.00 1.63–5.00 1.00–5.00 1.00–5.00 2.50–5.00
0.92
4.36 0.80 3.81 0.97 3.78 1.28
1.00–5.00 1.00–5.00 1.00–5.00
0.94
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Y. S. Roh and E. J. Lim: Pre-Course Simulation as a Predictor of Satisfaction
pre-course simulation, 3.81 (SD ¼ 0.97) for self-directed pre-course e-learning, and 3.78 (SD ¼ 1.28) for clinical placement skill performance.
satisfaction. Of these four variables, pre-course simulation made the largest contribution to learner satisfaction, accounting for 29.1% of the variance.
Relationship between course satisfaction and other study variables
Discussion
Course satisfaction was significantly related to satisfaction with pre-course simulation (r ¼ 0.54, p < 0.001), observation during the clinical placement (r ¼ 0.47, p < 0.001), clinical placement skill performance (r ¼ 0.45, p < 0.001), and selfdirected pre-course e-learning (r ¼ 0.38, p < 0.001).
Factors influencing course satisfaction Normality, independence of errors, and multicollinearity were examined to verify that the assumptions of multiple regression analysis were met. The Durbin–Watson d value was 1.907, which is between the two critical values of 1.5 and 2.5; thus, it can be concluded that there was no first-order linear auto-correlation in the multiple linear regression data. The plot also indicated that all variables had either a tolerance below 0.1 or a variance inflation factor over 10; in other words, there was no multicollinearity. Table 2 presents the results of the stepwise multiple linear regression and the overall fit statistics. Pre-course simulation, clinical placement skill performance, observation during the clinical placement, and pre-course e-learning were entered in sequence. These four variables together accounted for 47.2% of the variance in the final model (F ¼ 64.26, p < 0.001). Pre-course simulation (β ¼ 0.269, t ¼ 5.164, p < 0.001), clinical placement skill performance (β ¼ 0.299, t ¼ 6.646, p < 0.001), observation during the clinical placement (β ¼ 0.273, t ¼ 5.743, p < 0.001), and pre-course e-learning (β ¼ 0.191, t ¼ 4.037, p < 0.001) significantly explained learner
Table 2
This study explored the relationships between pre-course simulation, pre-course e-learning, skill performance during clinical placement, observation during clinical placement, and student satisfaction with an emergency nursing clinical course. The results suggest that precourse simulation substantially explained the variance in student satisfaction when compared to clinical placement skill performance, observation during the clinical placement, and pre-course e-learning. Pre-course simulation was the most significant contributor to course satisfaction. Study participants indicated that they were generally satisfied with the precourse simulation, a finding consistent with the literature (Brewer, 2011; Cook et al., 2012). The feedback from students following simulation-based training is generally good (Cook et al., 2012). In a recent evaluation of simulation-based training for nursing students, participants reported that practice with a human patient simulator boosted clinical skills, knowledge acquisition, and critical thinking; it also induced feelings of satisfaction and self-confidence (Brewer, 2011). Thus, the results of this study support previous evaluations suggesting that the integration of simulation with clinical placement has the potential to promote effective student learning in the current clinical environment. The multiple regression analysis undertaken in this investigation revealed that skill performance, observation, and pre-course e-learning also contributed significantly to the variance in learner satisfaction, although pre-course simulation explained more of the variance than any of these other factors. A previous study revealed that the opportunity to practice skills, supervision, and
Factors influencing course satisfaction (N ¼ 284)
Variable Constant Pre-course simulation Clinical placement skill performance Observation during the clinical placement Pre-course e-learning
B
SE
0.579 0.300 0.129 0.189 0.108
0.210 0.058 0.019 0.033 0.027
β
t
0.269 0.299 0.273 0.191
2.758* 5.164† 6.646† 5.743† 4.037†
R2 change
Adjusted R2
F
0.472
64.26†
0.291 0.097 0.061 0.030
Notes: *p ¼ 0.006; †p < 0.001.
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assessment is less than optimal for competent skill development in current clinical placements (Stayt & Merriman, 2012). In another study, nursing students reported that a multimodal approach was a useful way to prepare for clinical placement (Bloomfield, Cornish, Parry, Pegram, & Moore, 2013). The results of the study reported herein support this finding, confirming that a multimodal approach may be a useful strategy for overcoming some of the limitations associated with the sole use of either pre-course learning modalities or traditional clinical teaching strategies. Of the six factors comprising “course satisfaction,” “faculty,” and “environment” elicited the highest mean scores. According to the results of a previous study, despite acknowledging limitations in the levels of supervision they received, many students commended their mentors for being dedicated, motivated, and supportive (Stayt & Merriman, 2012). Other studies have demonstrated that students value the support provided by their mentors and are highly satisfied with their clinical experiences if they have a good mentor (Chuan & Barnett, 2012; Henderson et al., 2012). One previous study pointed to a need for explicit linkages between education and practice environments and structured support for clinical mentors (O’Driscoll, Allan, & Smith, 2010). From a literature review, Tanda and Denham (2009) concluded that the judicious use of a skills laboratory, consistent clinical placement, supportive clinical learning environments, and effective coaching by clinical educators positively affect student outcomes. Brown et al. (2011) reported that task orientation, student involvement, personalization, and innovation were significant predictors of health science students’ self-reported satisfaction with their clinical learning environment. Given all of these findings, strategies that focus on improving the amount of faculty support and the quality of the clinical learning environment may optimize overall student satisfaction with their clinical education experience. The use of simulation to allow students to practice clinical decision-making without causing patient harm is seen by many as crucial to promoting safe educational practices (Jeffries, 2005). The personal involvement of students in the management of an emergency event may lead to experiential learning and should have a positive impact on the students’ learning motivation (Pelaccia et al., 2009). However, it is unrealistic to expect that one or two episodes of teaching and learning using simulation are sufficient to produce competence or proficiency. Learners still need to apply their learning in the real world, under supervision, with feedback so that their skills base becomes consolidated, refined, and adaptable
(Ker & Bradley, 2010). The results of this study suggest that the integration of simulation into clinical curricula is a valuable and positively evaluated modality for preparing nursing students for clinical placement.
Limitations This study was conducted with a convenience sample of nursing students in their second year of a 3-year program at one nursing school. Consequently, the results, while informative, are not directly generalizable to other nursing students or other nursing schools. Future research ideally would include experimental designs aimed at investigating causality, particularly regarding the factors related to nursing students’ satisfaction with their emergency clinical courses. Importantly, 47% of the variance in course satisfaction was explained by the combined effect of the four predictors. The remaining 53% was explained by factors not included in the regression model; the identification of the predictors that comprise this unexplained variance should be the subject of future studies.
Strengths Many previous studies have demonstrated that simulation education can effectively improve nursing students’ practical competence. Despite an increase in the use of simulation and positive educational outcomes in nursing education, only a few studies have identified relationships between pre-course simulation and clinical course satisfaction. The study reported herein used a relatively large sample to highlight the integration of simulation, a valuable and positively evaluated modality, into the preparatory curriculum for clinical placement of nursing students. Study results suggest that the multimodal approach, including pre-course simulation, clinical skill performance, observation, and pre-course e-learning, is a potentially useful strategy for overcoming some of the limitations associated with the sole use of either precourse learning modalities or traditional clinical teaching strategies.
Conclusions This study found that pre-course simulation, clinical skill performance, observation, and pre-course e-learning significantly influenced learner satisfaction with an
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Y. S. Roh and E. J. Lim: Pre-Course Simulation as a Predictor of Satisfaction
emergency nursing clinical course, with pre-course simulation exerting the greatest effect. These results suggest that the integration of simulation into the clinical curricula is a valuable and positively evaluated modality for preparing nursing students for clinical placement.
Implications/recommendations The findings reported herein suggest that pre-course simulation is a potentially useful strategy to improve student satisfaction with clinical education. Moreover, integrating pre-course simulation with pre-course e-learning, clinical skill performance, and observation achieves a greater effect than the sole use of either simulation or traditional clinical teaching strategies. Student satisfaction is an important element of the effectiveness of clinical placement. Therefore educators designing and implementing clinical courses may consider integrating active learning strategies such as pre-course simulation with tailored clinical placement experiences to optimize student satisfaction. Further research is needed to assess the long-term outcomes of using simulation to prepare nursing students for clinical placement.
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emergency department on the motivation of health care students to learn. Medical Education, 43(5), 462–469. doi:10.1111/ j.1365-2923.2009.03356.x Perkins, G. D., Fullerton, J. N., Davis-Gomez, N., Davies, R. P., Baldock, C., Stevens, H., … Lockey, A. S. (2010). The effect of pre-course e-learning prior to advanced life support training: A randomised controlled trial. Resuscitation, 81(7), 877–881. doi:10.1016/j.resuscitation.2010.03.019 Polit, D. F., & Beck, C. T. (2011). Nursing research: Generating and assessing evidence for nursing practice (9th ed.). Philadelphia, PA: Lippincott Williams & Wilkins. Rochester, S., Kelly, M., Disler, R., White, H., Forber, J., & Matiuk, S. (2012). Providing simulation experiences for large cohorts of 1st year nursing students: Evaluating quality and impact. Collegian, 19(3), 117–124. doi:10.1016/j.colegn.2012.05.004 Stayt, L. C., & Merriman, C. (2012). A descriptive survey investigating pre-registration student nurses’ perceptions of clinical skill development in clinical placements. Nurse Education Today, 33(4), 425–430. doi:10.1016/j. nedt.2012.10.018 Tanda, R., & Denham, S. A. (2009). Clinical instruction and student outcomes. Teaching and Learning in Nursing, 4(4), 139–147. Watson, K., Wright, A., Morris, N., McMeeken, J., Rivett, D., Blackstock, F., … Jull, G. (2012). Can simulation replace part of clinical time? Two parallel randomised controlled trials. Medical Education, 46(7), 657–667. doi:10.1111/j.13652923.2012.04295.x Williams, B., French, J., & Brown, T. (2009). Can interprofessional education DVD simulations provide an alternative method for clinical placements in nursing? Nurse Education Today, 29(6), 666–670. doi:10.1016/j.nedt.2009.02.008
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Research Article Young Sook Roh and Eun Ju Lim*
Pre-Course Simulation as a Predictor of Satisfaction with an Emergency Nursing Clinical Course Abstract: Recent research suggests that simulation education can effectively improve nursing students’ practical competence and can enhance educational outcomes. But very few studies have identified the relationships between pre-course simulation and course satisfaction. The purpose of this study was to determine whether pre-course simulations and other advanced learning modalities (i.e. pre-course e-learning, observation, and clinical placement skill performance) predicted students’ satisfaction with an emergency nursing clinical course. Second-year Korean nursing students (N ¼ 284) participated in an integrated clinical course consisting of selfdirected pre-course e-learning, a 2-hour pre-course simulation, and an 80-hour emergency room clinical placement with observation. Multiple regression analyses found that pre-course simulation, clinical placement skill performance, observation during the clinical placement, and pre-course e-learning accounted for 47.2% of the variance in course satisfaction. Notably, pre-course simulation made the largest contribution to course satisfaction, accounting for 29.1% of the variance. Pre-course simulation, skill performance, observation, and precourse e-learning all significantly influenced learner satisfaction. Findings suggest that integrating simulation into the clinical curriculum may enhance clinical course satisfaction. Keywords: simulation, course satisfaction, emergency nursing, clinical placement DOI 10.1515/ijnes-2013-0083
*Corresponding author: Eun Ju Lim, Red Cross College of Nursing, Chung-Ang University (CAU), 84 Heukseok-ro Dongjak-gu, Seoul 156-756, Korea, E-mail: [email protected] Young Sook Roh, Red Cross College of Nursing, Chung-Ang University (CAU), 84 Heukseok-ro Dongjak-gu, Seoul 156-756, Korea, E-mail: [email protected]
Clinical placement is thought to be necessary for nursing students to become competent in their profession (AlKandari, Vidal, & Thomas, 2009; Williams, French, & Brown, 2009). Nursing students have reported that clinical exposure enhanced their orientation to the field and promoted role actualization (Michalec, Diefenbeck, & Mahoney, 2013). However, one descriptive survey demonstrated that nursing students’ opportunities to practice certain critical skills, supervision, and assessment within their clinical placements were less than optimal for competent skill development (Stayt & Merriman, 2012). The development of sustainable approaches for enhancing nursing students’ clinical learning experience is an international concern (Newton, Jolly, Ockerby, & Cross, 2012). Increasing student numbers, reduced length of inpatient stay, and the reduced availability of clinical teachers have fueled the need for clinical education that involves simulation (Issenberg, McGaghie, Petrusa, Lee Gordon, & Scalese, 2005). A recent meta-analysis demonstrated that simulation-based medical education with deliberate practice is superior to traditional clinical education approaches in the achievement of specific clinical skill acquisition goals (McGaghie, Issenberg, Cohen, Barsuk, & Wayne, 2011). However, some have suggested that simulation must be used alongside, and linked closely with, clinical practice (Kneebone, Scott, Darzi, & Horrocks, 2004; McGaghie, Siddall, Mazmanian, & Myers, 2009; Meyer, Connors, Hou, & Gajewski, 2011). Regretfully, evidence in support of the idea that simulation is a valuable supplement to clinical placement is sparse. The measurement of a learner’s course satisfaction can convince stakeholder groups to support specific learning modalities (Kirkpatrick & Kirkpatrick, 2006). However, the reported positive outcomes of simulation or other learning modalities used in clinical education do not dilute concerns about whether these positive outcomes directly lead to improved satisfaction with a clinical course. Thus, further study is required to afford
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evidence that this blended learning approach is a viable supplement to traditional learning methods.
Literature review Currently, there is much discussion worldwide regarding the potential for simulation to replace some of the required clinical placement hours (Arthur, Kable, & Levett-Jones, 2011). One study has provided evidence that clinical education in simulated learning environments can result in a 25% reduction in clinical time with real patients without compromising the students’ attainment of necessary professional competencies (Watson et al., 2012). A recent meta-analysis has demonstrated that simulation-based medical education with deliberate practice is superior to traditional clinical medical education in the achievement of specific clinical skill acquisition goals (McGaghie et al., 2011). However, some educators suggest that simulation must be used alongside, and linked closely with, clinical practice (Kneebone et al., 2004; Meyer et al., 2011). Others argue that medical simulation can be used only to complement, not replace, educational activities based on real patient care experiences (McGaghie et al., 2009). A focus group interview with key midwifery academics has suggested that simulation could be unrealistic (McKenna et al., 2011). Despite an increase in the use of simulation with positive educational outcomes in nursing education (Rochester et al., 2012), very few studies have found associations between pre-placement simulation and clinical placement (Khalaila, 2014; Williams et al., 2009). The potential of e-learning strategies also has been considered along with simulation, as e-strategies afford wider access and availability than “classical” learning techniques (Perkins et al., 2010). A meta-analysis by Cook et al. (2010) found that e-learning increased students’ control of the content, place, and time of learning. Other education-related studies have demonstrated that pre-course preparatory strategies, including computerassisted learning tutorials, enhance knowledge acquisition and reduce classroom time (Bhanji et al., 2010). Over 70% of healthcare providers reported that pre-course elearning improved their understanding of an Advanced Life Support course’s key learning domains (Perkins et al., 2010). A study of digital video disk simulations found that nursing students perceived that this approach helped to familiarize them with clinical placements (Williams et al., 2009). However, one systematic review of the benefits of e-learning for nurses and student nurses
afforded no results on these individuals’ satisfaction with their e-learning experiences, because the satisfaction data from three studies were not available (Lahti, Hätönen, & Välimäki, 2013). In general, a literature review has indicated that the judicious use of a skills laboratory, consistent clinical placement, supportive clinical learning environment, and effective coaching by clinical educators positively affected student outcomes (Tanda & Denham, 2009). In addition, Brown et al. (2011) reported that task orientation, student involvement, personalization, and innovation were significant predictors of health science students’ self-reported satisfaction with clinical learning environments. As well, effective clinical learning is believed to require integrating nursing students into ward activities, engaging staff in the process of addressing individual student learning needs, supportive clinical learning activities, and implementing innovative teaching approaches (Henderson, Cooke, Creedy, & Walker, 2012). Despite the fact that innovative teaching approaches, including pre-course simulation and e-learning, increasingly have been adopted in order to foster student-centered clinical learning, very few studies have found associations between pre-placement simulation and clinical placement (Khalaila, 2014; Williams et al., 2009). Learner satisfaction and its relationships with innovative teaching approaches and clinical placement are not yet fully understood. Further research is needed to inform evidence-based teaching approaches that enhance nursing students’ clinical learning experiences. Accordingly, the purpose of this study was to determine whether precourse simulation and other learning modalities (specifically, pre-course e-learning, observation, and clinical placement skill performance) predicted student satisfaction with an emergency nursing clinical course.
Methods Design A cross-sectional descriptive survey design employing regression analysis was selected to advance knowledge from previous research by exploring the relationships between pre-course simulation, e-learning, observation, clinical placement skill performance, and student satisfaction with an emergency nursing clinical course. The emergency nursing clinical course consisted of a self-directed pre-course e-learning program presenting an orientation to emergency nursing (i.e. environment, patient
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Y. S. Roh and E. J. Lim: Pre-Course Simulation as a Predictor of Satisfaction
management, equipment, medication, resuscitation, and medical, surgical, and toxicological emergencies), a 2-hour pre-course simulation providing cardiopulmonary resuscitation (CPR) skills training, and an 80-hour clinical placement in an emergency department.
Participants A sample size calculation using G*power 3.0.10 software (Faul, Erdfelder, Buchner, & Lang, 2009) indicated that with four predictors in a stepwise multiple regression analysis, a minimum sample size of 129 is required to obtain a moderate effect size of 0.15, with a significance of 0.05 and a power of 0.95, using F-tests. To meet this requirement, a convenience sample of 290 second-year students enrolled in an emergency nursing clinical course was approached and recruited for participation, regardless of age or gender, at the time of their clinical rotation (recruitment rate ¼ 100%). The participants thus came from 14 groups of 20–24 students rotated every 2 weeks through an emergency department for clinical placement. Six incomplete questionnaires were excluded from the analyses; thus, the data from 284 nursing students (participation rate ¼ 98%) were analyzed.
Procedure Approval of research ethics was granted by the Ethics Committee of the college of nursing. All participants signed an informed consent form before entering the study, which took place from September 2011 to June 2012. The pre-course simulation of 2 hours of CPR skills training was conducted 1 week prior to the student’s emergency department rotation. The same instructor participated in all pre-course simulation sessions to ensure that the lecture and training style was consistent for all participants. All participants studied the principles of and the algorithm for adult basic life support (BLS) before their pre-course simulation session. They then received a 30-minute didactic lecture about the Korea National Adult BLS Guidelines, as well as 1 hour of CPR skills training that included hands-on practice using videomediated, practice-while-watching instruction. The CPR skills training was conducted using a static manikin, with only a torso and a head, which was placed in a hospital bed. After the training, all participants tested their CPR skills on sensor-equipped adult manikins (Resusci Anne™; Laerdal Medical, Stavanger, Norway). At the end of the pre-course simulation session, the instructor
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provided each student with feedback based on performance data printed from the SkillReporter. This feedback allowed the students to reflect on and analyze their behavior. One week after this pre-course simulation experience, sub-groups of two to six of the 20–24 nursing students in each clinical rotation were assigned to 8hour long day or evening shifts for 2 weeks in each of two semesters at one of seven different emergency departments. These seven emergency departments had 22–46 patient beds. Three faculty members from the college of nursing provided the 4 hours of clinical teaching per week, with each faculty member teaching in two or three of the seven emergency departments on either the day or evening shift. During the first week of this clinical experience, the student participants used an electronic medical record system to record the progress of their assigned patients, and the faculty provided them with feedback regarding their clinical experiences. Faculty demonstrated how to use the emergency department equipment and crash cart, and the students practiced resuscitation skills (e.g. defibrillation and assisting with intubation) under faculty supervision. Student participants’ clinical skills and behaviors in the emergency department were assessed using the clinical performance checklists developed by the authors. During the second week in the emergency department, students presented their clinical cases and received feedback from their peers and faculty during clinical teaching sessions held in a conference room. The clinical performance checklists were reviewed to promote reflection on the students’ clinical experiences. All participants filled out self-administered instruments after completing the clinical course.
Measures Course satisfaction was measured using the Satisfaction with Clinical Course Scale (Lee, Kim, & Kim, 2004). This instrument consists of 30 items assessing six factors (Faculty, Environment, Time, Content, Preceptor, and Evaluation) rated on 5-point Likert-type scales (1 ¼ very dissatisfied; 5 ¼ very satisfied). Higher mean scores indicate higher satisfaction. In a previous study, the instrument had a Cronbach’s α value of 0.87 (Lee et al., 2004). The Cronbach’s α value in this study was 0.92. Satisfaction with pre-course simulation was measured using the Satisfaction with Simulation Experience Scale (Levett-Jones et al., 2011). This 18-item instrument
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Y. S. Roh and E. J. Lim: Pre-Course Simulation as a Predictor of Satisfaction
assesses satisfaction with three aspects of simulation, namely, Debriefing and Reflection, Clinical Reasoning, and Clinical Learning, on 5-point Likert-type scales (1 ¼ very dissatisfied; 5 ¼ very satisfied). Higher mean scores indicate higher satisfaction. In a previous study, this instrument had a Cronbach’s α value of 0.77 (LevettJones et al., 2011). The Cronbach’s α in the study reported herein was 0.94. Satisfaction with the learning modality was measured by asking the participants to report their level of satisfaction for three learning modalities, pre-course e-learning, observation, and clinical placement skill performance, rating each on a 5-point Likert-type scale (1 ¼ very dissatisfied; 5 ¼ very satisfied). Higher mean scores indicate higher satisfaction.
the strongest bivariate correlation with the dependent variable. The second variable to enter the equation was the one that produced the largest increase to R2 when used simultaneously with the variable selected in the first step. This procedure continued until no additional predictor significantly increased the value of R2 (Polit & Beck, 2011). The significance threshold was set at 0.05 for all tests. There were no missing data in the responses of the 284 student participants ultimately in the study.
Results Demographic characteristics of the nursing students
Data analysis All statistical analyses were performed using SPSS version 20.0 (SPSS Inc., Chicago, IL). Descriptive statistics were calculated for the general characteristics of the participants and the study variables. Pearson correlation coefficients were used to assess the relationships between course satisfaction and the other study variables. Stepwise multiple regression analyses were employed to determine whether any of the four learning modalities (i.e. pre-course simulation, pre-course e-learning, observation, and clinical placement skill performance) predicted satisfaction with an emergency nursing clinical course. For the stepwise multiple regression, predictors were entered into the regression equation in sequential order from the highest to the lowest influences on R2. The first step was to select the single best predictor of the dependent variable, that is, the independent variable with
Table 1
Of the 284 participants, 91% (N ¼ 259) were women. Their ages ranged from 18 to 49 years, with a mean age of 21.7 years (SD ¼ 4.48). Prior to the study, none of the participating students had any experience working with sensorequipped adult manikins.
Descriptive statistics The descriptive statistics for the study variables are presented in Table 1. On a 5-point scale, the overall mean course satisfaction score was 3.56 (SD ¼ 0.55). Students gave the highest scores to the factor “faculty” (mean ¼ 4.04, SD ¼ 0.69) and the lowest to “evaluation” (mean ¼ 3.12, SD ¼ 0.84). On a 5-point scale, the overall mean score was 4.36 (SD ¼ 0.80) for observation during the clinical placement, 4.18 (SD ¼ 0.50) for satisfaction with
Descriptive statistics for the study variables (N ¼ 284)
Variable Course satisfaction Faculty Environment Time Content Preceptor Evaluation Satisfaction with pre-course simulation Satisfaction with learning modality Observation during the clinical placement Pre-course e-learning Clinical placement skill performance
Mean SD
Range
Cronbach’s α
3.56 0.55 4.04 0.69 3.96 0.60 3.40 0.88 3.39 0.60 3.25 1.05 3.12 0.84 4.18 0.50
1.90–5.00 1.67–5.00 2.13–5.00 1.00–5.00 1.63–5.00 1.00–5.00 1.00–5.00 2.50–5.00
0.92
4.36 0.80 3.81 0.97 3.78 1.28
1.00–5.00 1.00–5.00 1.00–5.00
0.94
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Y. S. Roh and E. J. Lim: Pre-Course Simulation as a Predictor of Satisfaction
pre-course simulation, 3.81 (SD ¼ 0.97) for self-directed pre-course e-learning, and 3.78 (SD ¼ 1.28) for clinical placement skill performance.
satisfaction. Of these four variables, pre-course simulation made the largest contribution to learner satisfaction, accounting for 29.1% of the variance.
Relationship between course satisfaction and other study variables
Discussion
Course satisfaction was significantly related to satisfaction with pre-course simulation (r ¼ 0.54, p < 0.001), observation during the clinical placement (r ¼ 0.47, p < 0.001), clinical placement skill performance (r ¼ 0.45, p < 0.001), and selfdirected pre-course e-learning (r ¼ 0.38, p < 0.001).
Factors influencing course satisfaction Normality, independence of errors, and multicollinearity were examined to verify that the assumptions of multiple regression analysis were met. The Durbin–Watson d value was 1.907, which is between the two critical values of 1.5 and 2.5; thus, it can be concluded that there was no first-order linear auto-correlation in the multiple linear regression data. The plot also indicated that all variables had either a tolerance below 0.1 or a variance inflation factor over 10; in other words, there was no multicollinearity. Table 2 presents the results of the stepwise multiple linear regression and the overall fit statistics. Pre-course simulation, clinical placement skill performance, observation during the clinical placement, and pre-course e-learning were entered in sequence. These four variables together accounted for 47.2% of the variance in the final model (F ¼ 64.26, p < 0.001). Pre-course simulation (β ¼ 0.269, t ¼ 5.164, p < 0.001), clinical placement skill performance (β ¼ 0.299, t ¼ 6.646, p < 0.001), observation during the clinical placement (β ¼ 0.273, t ¼ 5.743, p < 0.001), and pre-course e-learning (β ¼ 0.191, t ¼ 4.037, p < 0.001) significantly explained learner
Table 2
This study explored the relationships between pre-course simulation, pre-course e-learning, skill performance during clinical placement, observation during clinical placement, and student satisfaction with an emergency nursing clinical course. The results suggest that precourse simulation substantially explained the variance in student satisfaction when compared to clinical placement skill performance, observation during the clinical placement, and pre-course e-learning. Pre-course simulation was the most significant contributor to course satisfaction. Study participants indicated that they were generally satisfied with the precourse simulation, a finding consistent with the literature (Brewer, 2011; Cook et al., 2012). The feedback from students following simulation-based training is generally good (Cook et al., 2012). In a recent evaluation of simulation-based training for nursing students, participants reported that practice with a human patient simulator boosted clinical skills, knowledge acquisition, and critical thinking; it also induced feelings of satisfaction and self-confidence (Brewer, 2011). Thus, the results of this study support previous evaluations suggesting that the integration of simulation with clinical placement has the potential to promote effective student learning in the current clinical environment. The multiple regression analysis undertaken in this investigation revealed that skill performance, observation, and pre-course e-learning also contributed significantly to the variance in learner satisfaction, although pre-course simulation explained more of the variance than any of these other factors. A previous study revealed that the opportunity to practice skills, supervision, and
Factors influencing course satisfaction (N ¼ 284)
Variable Constant Pre-course simulation Clinical placement skill performance Observation during the clinical placement Pre-course e-learning
B
SE
0.579 0.300 0.129 0.189 0.108
0.210 0.058 0.019 0.033 0.027
β
t
0.269 0.299 0.273 0.191
2.758* 5.164† 6.646† 5.743† 4.037†
R2 change
Adjusted R2
F
0.472
64.26†
0.291 0.097 0.061 0.030
Notes: *p ¼ 0.006; †p < 0.001.
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assessment is less than optimal for competent skill development in current clinical placements (Stayt & Merriman, 2012). In another study, nursing students reported that a multimodal approach was a useful way to prepare for clinical placement (Bloomfield, Cornish, Parry, Pegram, & Moore, 2013). The results of the study reported herein support this finding, confirming that a multimodal approach may be a useful strategy for overcoming some of the limitations associated with the sole use of either pre-course learning modalities or traditional clinical teaching strategies. Of the six factors comprising “course satisfaction,” “faculty,” and “environment” elicited the highest mean scores. According to the results of a previous study, despite acknowledging limitations in the levels of supervision they received, many students commended their mentors for being dedicated, motivated, and supportive (Stayt & Merriman, 2012). Other studies have demonstrated that students value the support provided by their mentors and are highly satisfied with their clinical experiences if they have a good mentor (Chuan & Barnett, 2012; Henderson et al., 2012). One previous study pointed to a need for explicit linkages between education and practice environments and structured support for clinical mentors (O’Driscoll, Allan, & Smith, 2010). From a literature review, Tanda and Denham (2009) concluded that the judicious use of a skills laboratory, consistent clinical placement, supportive clinical learning environments, and effective coaching by clinical educators positively affect student outcomes. Brown et al. (2011) reported that task orientation, student involvement, personalization, and innovation were significant predictors of health science students’ self-reported satisfaction with their clinical learning environment. Given all of these findings, strategies that focus on improving the amount of faculty support and the quality of the clinical learning environment may optimize overall student satisfaction with their clinical education experience. The use of simulation to allow students to practice clinical decision-making without causing patient harm is seen by many as crucial to promoting safe educational practices (Jeffries, 2005). The personal involvement of students in the management of an emergency event may lead to experiential learning and should have a positive impact on the students’ learning motivation (Pelaccia et al., 2009). However, it is unrealistic to expect that one or two episodes of teaching and learning using simulation are sufficient to produce competence or proficiency. Learners still need to apply their learning in the real world, under supervision, with feedback so that their skills base becomes consolidated, refined, and adaptable
(Ker & Bradley, 2010). The results of this study suggest that the integration of simulation into clinical curricula is a valuable and positively evaluated modality for preparing nursing students for clinical placement.
Limitations This study was conducted with a convenience sample of nursing students in their second year of a 3-year program at one nursing school. Consequently, the results, while informative, are not directly generalizable to other nursing students or other nursing schools. Future research ideally would include experimental designs aimed at investigating causality, particularly regarding the factors related to nursing students’ satisfaction with their emergency clinical courses. Importantly, 47% of the variance in course satisfaction was explained by the combined effect of the four predictors. The remaining 53% was explained by factors not included in the regression model; the identification of the predictors that comprise this unexplained variance should be the subject of future studies.
Strengths Many previous studies have demonstrated that simulation education can effectively improve nursing students’ practical competence. Despite an increase in the use of simulation and positive educational outcomes in nursing education, only a few studies have identified relationships between pre-course simulation and clinical course satisfaction. The study reported herein used a relatively large sample to highlight the integration of simulation, a valuable and positively evaluated modality, into the preparatory curriculum for clinical placement of nursing students. Study results suggest that the multimodal approach, including pre-course simulation, clinical skill performance, observation, and pre-course e-learning, is a potentially useful strategy for overcoming some of the limitations associated with the sole use of either precourse learning modalities or traditional clinical teaching strategies.
Conclusions This study found that pre-course simulation, clinical skill performance, observation, and pre-course e-learning significantly influenced learner satisfaction with an
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Y. S. Roh and E. J. Lim: Pre-Course Simulation as a Predictor of Satisfaction
emergency nursing clinical course, with pre-course simulation exerting the greatest effect. These results suggest that the integration of simulation into the clinical curricula is a valuable and positively evaluated modality for preparing nursing students for clinical placement.
Implications/recommendations The findings reported herein suggest that pre-course simulation is a potentially useful strategy to improve student satisfaction with clinical education. Moreover, integrating pre-course simulation with pre-course e-learning, clinical skill performance, and observation achieves a greater effect than the sole use of either simulation or traditional clinical teaching strategies. Student satisfaction is an important element of the effectiveness of clinical placement. Therefore educators designing and implementing clinical courses may consider integrating active learning strategies such as pre-course simulation with tailored clinical placement experiences to optimize student satisfaction. Further research is needed to assess the long-term outcomes of using simulation to prepare nursing students for clinical placement.
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