The Impact of Full-Scale Simulation Training Based on Kolb’s Learning Cycle on Medical Prehospital Emergency Teams: A Multilevel Assessment Study : Simulation in Healthcare

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Empirical Investigations

The Impact of Full-Scale Simulation Training Based on Kolb’s Learning Cycle on Medical Prehospital Emergency Teams

A Multilevel Assessment Study

Secheresse, Thierry MD; Pansu, Pascal PhD; Lima, Laurent PhD

Author Information
Simulation in Healthcare: The Journal of the Society for Simulation in Healthcare: October 2020 - Volume 15 - Issue 5 - p 335-340
doi: 10.1097/SIH.0000000000000461


Despite standardized initial management, the survival rate of out-of-hospital cardiac arrests remains low, at approximately 7%.1,2 In this context, high-fidelity full-scale simulation emerged as an alternative to other training methods.3 To date, many research reports suggest that full-scale simulation is useful and necessary to help hospital staff and other teams of health professionals be more effective.4,5 However, the issue of how and when to incorporate full-scale simulation in team training is often forgotten or set aside.6 Using simulation as a training tool, when taking the pedagogical approach into consideration throughout the simulation session, would strengthen the outcome of the training. Consequently, it is important to specify the theoretical framework underpinning simulation-based learning and look closely at how this framework is applied in the training session.

Among learning theories, Kolb’s experiential learning is frequently cited as the theoretical framework underpinning simulation-based learning.7–9 According to Kolb (1984), the experiential learning process is composed of 4 steps.10 In the first step, known as concrete experience, the learner experiences a situation (doing). In the second step, called reflective observation, the learner reviews the concrete experience on a personal basis (thinking about what was done). In the third step, called abstract conceptualization, the review of the concrete experience is translated into abstract concepts with implications for action. The learner forms new ideas resulting from the reflective observation step (thinking about a future experience). In the last step, known as active experimentation, the learner can plan and act out what has been learned in the previous steps. The learner applies the new ideas after having approved them and retained them in their long-term memory. According to Kolb (1984), learning occurs only when the 4 steps are present. Although the fourth step of active experimentation is as important as the other steps in the learning process, in most training programs, the active experimentation step is not a part of the training session. It is assumed to occur in actual clinical practice after the training session. Omission of this final step may damage the learning process, as indeed would omission of any of the other steps. The lack of the fourth step during the training program may be detrimental for 3 reasons. Firstly, if active experimentation does not occur, the concepts developed during the second and third steps could be lost. Secondly, when active experimentation occurs in actual clinical practice, it is highly likely that there will be no instructor feedback or control. Thirdly, facing a real situation without previous experience may hold the learner back from action. Integrating active experimentation during the training session is necessary to prevent these negative effects.

This study aimed to assess the effects of full-scale simulation training based on Kolb’s learning cycle on medical emergency eams. This program focused on out-of-hospital cardiac arrest and was designed for prehospital medical emergency teams. Having the entire team benefit from a simulation program could improve out-of-hospital cardiac arrest management in clinical practice.11 The effects of the training on knowledge and behavior were evaluated at individual level and at team level during the simulation session. The learning transfer of new behaviors was assessed in clinical practice in the 3 months after the training.


Ethics Statement

This study complies with the principles of the Declaration of Helsinki. It is defined as noninterventional research as mentioned in the French Public Health code. The participants volunteered for this study. Consent forms were signed by the participants and the trainers.

Study Design and Population

Medical emergency prehospital teams were enrolled. They underwent a simulation training program consisting of 3 simulations, each followed by a debriefing. Both individual and group effects of the training program were evaluated before and after the training. On the individual level, attitudes toward the simulation program and medical knowledge were evaluated. On the group level, team behavior and learning transfer were evaluated. The individual and the team levels were kept separate so as to be able to evaluate the effects of the training program on individual skills and the way they are expressed in the team.

The study was conducted in a French hospital that averages 3500 annual prehospital emergency interventions. These prehospital emergency interventions are carried out by prehospital medical emergency teams composed of a physician, a nurse, and a paramedic. This study was done as part of an ongoing training program for prehospital medical emergency teams. This program uses full-scale high-fidelity simulation. There is refresher training every year, allowing all medical and paramedical staff to benefit from simulation training on an annual basis. This program is an integral part of the hospital's healthcare quality improvement program.

Simulation Training Program

The duration of a simulation session was 4 hours. Two emergency physicians trained in simulation teaching supervised the simulation sessions. The topic of the training program was the management of out-of-hospital cardiac arrest. The main learning objectives were to improve technical skills, teamwork, and communication skills.

The program was based on a cycle of 3 simulations on the same theme. During the setting introduction (ie, presimulation briefing), the learners were informed about the simulation program and the training theme.

Simulation 1 concerned a 25-year-old man who was unconscious after taking drugs. Simulation 2 concerned a 42-year-old man with chest pain and sudden cardiac arrest in ventricular fibrillation. Simulation 3 concerned a 30-year-old male drug addict who was unconscious after an overdose. The sequence of simulation 3 was the same as for the first simulation. The scenarios were programmed in the simulation control computer as described in the Table 1.

TABLE 1 - Simulation Scenarios
Scenario Time, min Steps Simulator Status
Simulation 1
A 25-year-old man unconscious after taking drugs.
0–2 Initial patient management Unconscious
Respiratory rate: 6/min
Heart rate: sinusal, 50/ min
Blood pressure: 80/40 mm Hg
Pupils: bilateral myosis
2–4 Cardiac arrest with pulseless electrical activity Pulseless electrical activity
Heart rate: 10/min
Respiratory rate: 0
Blood pressure: 0
4–8 Cardiac arrest with ventricular fibrillation Ventricular fibrillation
8–12 ROSC Unconscious
Respiratory rate: 6/min
Heart rate: sinusal, 80/min
Blood pressure: 80/40 mm Hg
Pupils: bilateral median
Simulation 2
A 42-year-old man with chest pain and sudden cardiac arrest
0–10 Cardiac arrest with ventricular fibrillation Ventricular Fibrillation
10–12 ROSC Unconscious
Respiratory rate: 6/min
Heart rate: sinusal, 80/min
Blood pressure: 80/40 mm Hg
Pupils: bilateral median
Simulation 3
A 30-year-old male drug addict unconscious after an overdose
0–2 Initial patient management Unconscious
Respiratory rate: 6/min
Heart rate: sinusal, 50/min
Blood pressure: 80/40 mm Hg
Pupils: bilateral myosis
2–4 Cardiac arrest with pulseless electrical activity Pulseless electrical activity
Heart rate: 10/min
Respiratory rate: 0
Blood pressure: 0
4–8 Cardiac arrest with ventricular fibrillation Ventricular fibrillation
8–12 ROSC Unconscious
Respiratory rate: 6/min
Heart rate: sinusal, 80/min
Blood pressure: 80/40 mm Hg
Pupils: bilateral median
ROSC, return of spontaneous circulation.

All 3 simulations took place in a simulation center. The simulator used (Laerdal; Stavanger, Norway) allowed us to assess the required skills: thoracic compression, ventilation, electric shock treatment, intubation, and intravenous access. The high level of reliability was linked to the actual team composition and the use of real medical equipment. For example, we used a real emergency vehicle that was parked in front of the simulation center, so the team members had to go to the vehicle if additional equipment was needed.

The construct of this program was inspired by the main principles of Kolb’s experiential learning cycle (Fig. 1). The learners, as individuals and as team members, experienced all the steps of the learning cycle. The training session began with a simulation experience (concrete experience) that led to reflective observations during the description and analysis phases of the debriefing. These reflective observations were expected to translate into abstract concepts with implications for action during the analysis and the summary phases of the debriefing. Finally, the team and the individuals could actively and immediately test their new knowledge and skills in another simulation (active experimentation).

Simulation training session based on Kolb’s learning cycle.

A debriefing followed each scenario. A 4-phase structure of debriefing was used including the following steps.12,13 A reaction phase enabled the participants to express their feelings and to lower their emotional stress. A description phase recalled what happened during the simulation. An analysis phase explored the participants' state of mind and their actions during the simulation to confirm the correct interventions or to adjust the incorrect interventions. All the participants debated their points of view of the simulation to co-construct a shared mental model of out-of-hospital cardiac arrest management.14 Finally, a summary phase highlighted the take-away messages and planned future actions. Two emergency physicians trained in co-debriefing facilitated the debriefing.15

The first and the third simulations were identical (hypoxic cardiac arrest) and allowed us to measure the teams' behavior. The first one was a pretraining measurement and the third one was a posttraining measurement. The first simulation was an integral part of the teaching model insofar as we consider it was a real experience (having an experience), which during the debriefing led to reflective observations (reflecting on the experience) with implications for action (learning from the experience). In our training program, these implications must impact the second simulation. The spiral nature of this teaching model is singular in that it simultaneously processes both knowledge and debriefing measurements. The second simulation was also managing cardiac arrest, but with a different scenario: chest pain and ventricular fibrillation. The purpose of this second scenario was to use the main issues raised during the previous debriefing about advanced cardiac life support actions, such as managing no flow. This aimed to recontextualize the main teaching points and thus consolidate effective knowledge and behavior. Like the first scenario, it began with a simulation experience, which led to reflection and continued practice and learning opportunity. Because of the circular nature of Kolb’s cycle, the second simulation produced reflective observations with implications for action that made it possible to increase effectiveness in the third situation.

Evaluation Approach

Initially, individual participant satisfaction was collected by means of a satisfaction questionnaire (31 items) completed at the end of the program (see Supplemental Digital Content 1, Satisfaction Questionnaire, For each item, the participants rated their degree of satisfaction on a scale ranging from 0 (not at all satisfied) to 5 (completely satisfied).

Learning was evaluated by assessing individual medical knowledge (individual level) and team behavior (team level). Three medical knowledge tests included 10 multiple-choice items (one questionnaire for each profession) developed by 3 medical experts of the Emergency Medical Service. The items were generated from the cardiac arrest management literature review of the International Liaison Committee on Resuscitation.16 Five items were common to all 3 professions (basic life support) and 5 items were specific to each profession (see Supplemental Digital Content 2, Knowledge Test, The participants completed these medical knowledge tests at the beginning and at the end of the program.

Team behavior was evaluated by reviewing the videos of the first and third cardiac arrest clinical simulations. An emergency physician carried out the evaluation of the resuscitation. This physician was unaware if the video was from scenario 1 or 3 (blind test). A cardiac arrest grid was developed from the cardiac arrest simulation test (CASTest).17 This grid was adapted to the study scenarios and comprised of 34 items divided into 4 stages: bradypnea, pulseless electrical activity, ventricular fibrillation, and spontaneous recovery of cardiac activity (see Table, Supplemental Digital Content 3, Cardiac Arrest Grid, The scoring of each item was made on a 4-point scale from 0 (management not carried out or incorrect) to 3 (optimal management). For all items, specific behavior was described for each score to avoid subjectivity. This grid enabled a scoring of the team effectiveness on cardiac arrest management and also included measurement of the time taken for the different stages of resuscitation. As the scenarios programmed in the simulation control computer were the same during simulation 1 and 3, comparing the score between scenario 1 and 3 enabled us to evaluate learning in terms of team behavior and team effectiveness.

The learning transfer corresponds to behavioral changes in cardiac arrest management in clinical practice. It was evaluated from a qualitative analysis of the medical reports written by the physicians after each prehospital intervention. These reports describe how the team performed the resuscitation and allowed us to evaluate the overall management of a cardiac arrest at a team level. We compared medical reports of out-of-hospital cardiac arrest carried out in the 3 months before and after the simulation training program. A clinical resuscitation grid of 10 items corresponding to the main factors of cardiopulmonary resuscitation was developed from international recommendations on cardiac arrest management (see Table, Supplemental Digital Content 4, Clinical Resuscitation Grid, Each item was scored 0 (procedure not carried out or done incorrectly) or 1 (procedure carried out correctly). The item scores were added together to provide a score representing the overall management of a cardiac arrest. Among all the reports mentioning advanced cardiac life support on out-of-hospital cardiac arrest, we only took into account the reports completed by the same physician before and after the simulation training program.


The participants in this study were as follows: 24 paramedics, 26 nurses, and 22 physicians (Table 2). There were 26 simulation sessions. The results of 21 out-of-hospital medical emergency teams made up of the 3 professional categories were analyzed. Because of the unequal number of paramedics, nurses, and physicians, 5 sessions were organized with at least one of the participants who had already taken part in a previous simulation session in this program. To avoid team learning effects, these last 5 sessions were not analyzed.

TABLE 2 - Participants' Demographic Characteristics
Characteristics Paramedics
(n = 23)
(n = 26)
(n = 22)
Age, yr 41.8 (9.4) 40.8 (8.6) 37 (7.9)
Level of experience since graduation, yr 10.7 (9.3) 16 (8.8) 7.6 (7.7)
Level of experience in prehospital emergency unit, yr 6.7 (8.1) 6.3 (5.1) 6.6 (7.4)
Data are presented as mean (SD).


The results showed that the program was rated highly satisfactory. The mean scores per section varied from 4.48 (σ = 0.46) for the material to 4.90 (σ = 0.34) for the attitude of the trainers and very useful for their professional practice (M = 4.86, σ = 0.31).

Medical Knowledge

For medical knowledge, the results of repeated analyses of variance showed a positive significant effect of the training program for paramedics (F(1, 23) = 12,034, P = 0.002), nurses (F(1, 25) = 24,631, P < 0.001), and physicians (F(1, 21) = 30,996, P < 0.001). The results are presented in Table 3.

TABLE 3 - Knowledge Scores by Profession
Procedural Knowledge Score (0–10)
Profession Presimulation Training Postsimulation Training
Paramedics 5.25 (1.94, 3–9) 6.33 (1.90, 3–10)*
Nurses 6.69 (1.59, 4–10) 8.23 (0.95, 4–10)*
Physicians 5.64 (1.68, 3–9) 7.45 (1.53, 5–10)*
Data are presented as mean (SD, range).
*P < 0.003.

Team Behavior

Concerning the team behavior measured during the first and the third simulations, the results of repeated analyses of variance showed a significant increase in the total score for the initial management of the patient with cardiac arrest (F(1, 20) = 77.369, P < 0.001). This improvement was observed at all stages of patient management (Table 4).

TABLE 4 - Initial Management Scores by Simulation
Procedural Knowledge Score
Presimulation Training Postsimulation Training
Total score (0–102) 46.14 (12.49, 18–66) 71.76 (11.45, 40–89)*
Bradypnea (0–15) 7.10 (3.25, 1–10) 10.71 (3.27, 3–15)*
Pulseless electrical activity (0–33) 12.81 (4.69, 5–21) 19.90 (5.60, 6–27)*
Ventricular fibrillation (0–39) 20.90 (5.86, 8–33) 30.71 (5.03, 20–38)*
Spontaneous recovery of cardiac activity (0–15) 5.33 (4.51, 0–12) 10.43 (3.56, 2–15)*
Data are presented as mean (SD, range).
*P < 0.001.

The time taken to carry out the main resuscitation procedures was significantly reduced by the placement of the intravenous line (−98.3 seconds; F(1, 19) = 19.797, P < 0.001); intubation (−100.1 seconds F(1, 15) = 5.881, P = 0.028); and carrying out the first shock (−59 seconds; F(1, 13) = 6.911, P = 0.021).

Learning Transfer

To evaluate behavior changes in clinical practice, we examined all the out-of-hospital cardiac arrest reports collected before and after the simulation training. Nineteen presimulation training reports and 14 postsimulation training reports were analyzed. The results showed a significant improvement in the score for the overall management of cardiac arrest (Mpresimulation = 5.5 (σ = 1.7) vs. Mpostsimulation = 6.8 (σ = 1.8); Z = −1.787, P = 0.004). The improvement was significant for 2 specific items: duration of no flow (χ2 = 3.87, P = 0.049) and optimal ventilation frequency (χ2 = 5.02, P = 0.025).


Our results confirmed the scope of an out-of-hospital cardiac arrest simulation training program for prehospital emergency teams. This simulation-based program makes it possible to operationalize all the steps of Kolb’s cycle, especially the fourth step of active experimentation, which is often omitted in training programs. The evaluation at multiple levels18 of the impact of this program confirmed the usefulness of this type of training program for prehospital medical emergency teams.

Firstly, the usefulness perception of the training program was positive, which should reinforce motivation by encouraging commitment to the task.19 Secondly, the results showed an increase in medical knowledge and an improvement in team behavior in cardiac arrest management. This confirms the value of this teaching model for the acquisition both of theoretical knowledge and behavioral skills, even for emergency teams who regularly manage cardiac arrest.20,21 It is important to note that the participants had all been previously trained in Advanced Cardiac Life Support. Despite this prior knowledge, our results confirmed the importance of regular refresher training to optimize knowledge and effectiveness.22–24 Another positive result was the improvement in team coordination that led to faster implementation of critical procedures. Each prehospital emergency team member attended the simulation training program, regardless of their profession. As all the participants conducted several different cardiac arrest situations together, we expected the learning transfer in actual clinical team practice to be more effective because of the shared mental model of cardiac arrest management. This effectiveness was demonstrated in actual clinical practice, as we observed an improvement in total patient management after the training program. This was mostly observed on the no-flow duration report and on the optimal ventilation frequency. Before the simulation training, the out-of-hospital medical emergency teams were aware of the importance of no-flow time (an essential survival factor), but practicing together during simulation enhanced the team situation awareness of no-flow time management in clinical situations. These findings illustrate the effectiveness of the spiral nature of this teaching model, which was designed to consolidate knowledge and behaviors. In this program, we focused on a sequence of concrete experience in the initial simulation, reflective observation in the description and analysis debriefing phases, abstract conceptualization in the summary and planning debriefing phase, and active experimentation in the final simulation. Another strength of this training program is that it may facilitate standardization of practices within hospital and healthcare teams, who are required to undergo refresher simulation training every year.


This study has several limitations. First, behavioral assessment can be subject to evaluator subjectivity bias. To avoid this, specific behaviors were described for each score. A second limitation concerns the learning transfer evaluation method that used qualitative analysis of medical reports before and after the training program. Evaluating behavior is always difficult in the out-of-hospital context, but we must continue to look for ways to do this.25,26 Other measurements of learning transfer can be used, such as the use of a learning transfer prediction scale based on the one devised by Holton et al27 (2000) and cross-analyzing the data with direct observation of the interventions. Two other constraints when implementing the program evaluation will need to be dealt with in future studies: its short duration and the absence of a control group. Future studies should investigate the long-term effect of this kind of program using longitudinal design. Our results need to be studied with another Emergency Medical Service, such as paramedics only, before being generalized. Finally, implementing a program like this one for a whole department is time consuming and costly and may not therefore be feasible in every institution.


On the whole, the findings of this study support the usefulness of a simulation training program based on the principles of Kolb’s learning cycle for the ongoing training of prehospital emergency teams. According to these principles, the simulation produces reflective observations with implications for action that make it possible to increase effectiveness in another case, which in turn allows the participants to produce new reflective observations with new implications for action and so on and so forth. The last step, active experimentation, allows the learner to confirm the concepts developed in debriefing and in so doing, consolidate them. Future studies should explore the impact of simulation training based on Kolb learning cycle in other contexts and should also try to identify the elements of the training program, which optimize learning.


The authors would like to thank the prehospital emergency teams of the Metropole-Savoie Hospital who participated in this study, especially Dr Jorioz and Dr Usseglio for their role as instructors in this training program.


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Simulation; evaluating training; medical emergency team; Kolb’s experiential learning; cardiac arrest

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