Mindi Anderson

Research Regarding Debriefing as Part of the Learning Process

Introduction: Debriefing is a process involving the active participation of learners, guided by a facilitator or instructor whose primary goal is to identify and close gaps in knowledge and skills. A review of existing research and a process for identifying future opportunities was undertaken. Methods: A selective critical review of the literature on debriefing in simulation-based education was done. An iterative process of analysis, gathering input from audience participants, and consensus-based synthesis was conducted. Results: Research is sparse and limited in presentation for all important topic areas where debriefing is a primary variable. The importance of a format for reporting data on debriefing in a research context was realized and a “who, when, where, what, why” approach was proposed. Also, a graphical representation of the characteristics of debriefing studies was developed (Sim-PICO) to help guide simulation researchers in appropriate experimental design and reporting. Conclusion: A few areas of debriefing practice where obvious gaps that deserve study were identified, such as comparing debriefing techniques, comparing trained versus untrained debriefers, and comparing the effect of different debriefing venues and times. A model for publication of research data was developed and presented which should help researchers clarify methodology in future work.

Comparison of self-directed learning versus instructor-modeled learning during a simulated clinical experience.

Background: There are no reports in the literature that compare instructor-modeled learning to self-directed learning when simulation is used. Therefore, no evidence exists to know which approach is superior. This study aims to test the hypothesis that instructor-modeled learning is more effective compared with self-directed learning during a simulated clinical experience. Methods: This is a descriptive pilot study to compare instructor-modeled learning with self-directed learning during a clinical simulated experience. Four evaluation tools were used at three time points to evaluate knowledge, self-efficacy (self confidence), and behaviors. Results: Sixteen students participated. There were no statistically significant differences between the groups on the Knowledge Assessment Test. There were significant differences between the groups in the Self-Efficacy Tool (SET) at three times (time 1: P = 0.006, time 2: P = 0.008, time 3: P = 0.012). The only significance between the groups on the Technical Evaluation Tool was time to start Albuterol. The Behavioral Assessment Tool (BAT) showed significant differences between the groups in 8 out of 10 components of the tool. A strong correlation was observed between the overall score of the BAT and the SET Score. Conclusion: Although the small sample size prohibits definitive conclusions, the data suggest that instructor-modeled learning may be more effective than self-directed learning for some aspects of learning during a clinical simulated experience.

Playing it safe: simulated team training in the OR.

Health care professionals are expected to make prompt, accurate decisions in life-threatening emergency situations in the OR. Perioperative leaders are faced with the challenge of ensuring that OR team members are prepared to respond to potentially lethal emergencies, including rare and infrequent events. This article describes the importance of team training for all members of the OR team. The use of simulation is discussed as a tool to accomplish this goal. Steps for developing a simulation scenario are presented.

Alternative educational models for interdisciplinary student teams.

Background: Few studies compare instructor-modeled learning with modified debriefing to self-directed learning with facilitated debriefing during team-simulated clinical scenarios. Objective: To determine whether self-directed learning with facilitated debriefing during team-simulated clinical scenarios (group A) has better outcomes compared with instructor-modeled learning with modified debriefing (group B). Methods: This study used a convenience sample of students. The four tools used assessed pre/post knowledge, satisfaction, technical, and team behaviors. Thirteen interdisciplinary student teams participated: seven in group A and six in group B. Student teams consisted of one nurse practitioner student, one registered nurse student, one social work student, and one respiratory therapy student. The Knowledge Assessment Tool was analyzed by student profession. Results: There were no statistically significant differences within each student profession group on the Knowledge Assessment Tool. Group B was significantly more satisfied than group A (P = 0.01). Group B registered nurses and social worker students were significantly more satisfied than group A (30.0 ± 0.50 vs. 26.2 ± 3.0, P = 0.03 and 28.0 ± 2.0 vs. 24.0 ± 3.3, P = 0.04, respectively). Group B had significantly better scores than group A on 8 of the 11 components of the Technical Evaluation Tool; group B intervened more quickly. Group B had significantly higher scores on 8 of 10 components of the Behavioral Assessment Tool and overall team scores. Conclusion: The data suggest that instructor-modeling learning with modified debriefing is more effective than self-directed learning with facilitated debriefing during team-simulated clinical scenarios.

Effectiveness of 2 methods to teach and evaluate new content to neonatal transport personnel using high-fidelity simulation.

Neonatal transport team members undergo initial and periodic training to ensure knowledge and performance competencies. Given that various methods can be employed in this effort, it is important to evaluate how well new knowledge is learned and applied by transport team members and assess learner satisfaction. Self-paced modular learning and expert-modeled learning using high-fidelity simulations (HFSs) are 2 teaching/learning/evaluation tools for content application for team members. This article describes 2 educational approaches using simulated scenarios and evaluations. These experiences occurred in 2006, summer I (self-paced modular learning) and in 2007 for the same participants, summer II (expert-modeled learning). A group of experienced nurses, respiratory therapists, emergency medical technicians, and paramedics participated. Satisfaction was measured using a 5-point Likert scale. Effectiveness of HFS was evaluated using the Technical Evaluation Tool. Behavioral performance was assessed using the Behavioral Assessment Tool. The Technical Evaluation Tool showed that expert-modeled learning using HFS as a teaching/ learning/evaluation tool for application of content was effective as self-paced modular learning. Experienced participants appear to be satisfied with the use of HFS scenarios.

Can a virtual patient trainer teach student nurses how to save lives--teaching nursing students about pediatric respiratory diseases.

Introduction Virtual environments offer a variety of benefits and may be a powerful medium with which to provide nursing education. The objective of this study was to compare the achievement of learning outcomes of undergraduate nursing students when a virtual patient trainer or a traditional lecture was used to teach pediatric respiratory content. Methods This was a randomized, controlled, posttest design. A virtual pediatric hospital unit was populated with four virtual pediatric patients having different respiratory diseases that were designed to meet the same learning objectives as a traditional lecture. The study began in Spring 2010 with 93 Senior I, baccalaureate nursing students. Students were randomized to receive either a traditional lecture or an experience with a virtual patient trainer. Students’ knowledge acquisition was evaluated using multiple-choice questions, and knowledge application was measured as timeliness of care in two simulated clinical scenarios using high-fidelity mannequins and standardized patients. Results Ninety-three students participated in the study, of which 46 were in the experimental group that received content using the virtual patient trainer. After the intervention, students in the experimental group had significantly higher knowledge acquisition (P = 0.004) and better knowledge application (P = 0.001) for each of the two scenarios than students in the control group. Conclusions The purpose of this project was to compare a virtual patient trainer to a traditional lecture for the achievement of learning outcomes for pediatric respiratory content. Although the virtual patient trainer experience produced statistically better outcomes, the differences may not be clinically significant. The results suggest that a virtual patient trainer may be an effective substitute for the achievement of learning outcomes that are typically met using a traditional lecture format. Further research is needed to understand how best to integrate a virtual patient trainer into undergraduate nursing education.

Application of the Health Belief Model to bariatric surgery.

This article discusses the challenging issues faced by women who are diagnosed as being medically obese (i.e., body mass index > or = 30 kg/m). Recently cited as the new national epidemic by some healthcare professionals, obesity can lead to other medical comorbidities, including chronic heart problems, joint deterioration, sleep apnea, depression, and lower self-esteem. On the basis of medical records released by Methodist Hospital in Houston, TX, most patients who seek medical or surgical health options to control obesity are women between 25 and 40 years of age. This article discusses bariatric surgery as a weight loss option and applies the Health Belief Model to explain why patients are choosing this option to help them change eating behaviors. Interventions for nurses and other healthcare professionals are addressed.

Emotion detection via discriminative kernel method

Human emotion detection is of substantial importance in diverse pervasive applications in assistive environments. Because facial expressions provide a key mechanism for understanding and conveying emotion, automatic emotion detection through facial expression recognition has attracted increased attention in both scientific research and practical applications in recent years. Traditional facial expression recognition methods normally use only one type of facial expression data, either static data extracted from one single face image or motion dependent data obtained from dynamic face image sequences, but seldom employ both. In this work, we propose a novel Discriminative Kernel Facial Emotion Recognition (DKFER) method to integrate these two types of facial expression data using a hybrid kernel, such that the advantages of both of them are exploited. In addition, by using Linear Discriminant Analysis (LDA) to transform the two types of original facial expression data into two more discriminative lower-dimensional subspaces, the succeeding classification for emotion detection can be carried out in a more efficient and effective way. Encouraging experimental results in empirical studies demonstrate the practical usage of the proposed DKFER method for emotion detection.