Cognitive Load, Anxiety, and Performance During a Simulated Subarachnoid Block

Mood states and academic performance in the objective structured clinical examination. The mediating effect of self-efficacy

BACKGROUND

The role of self-efficacy on academic outcomes in clinical simulation evaluations is well-known. However, no previous studies have reported the mediating effect of self-efficacy in the link between mood state and academic performance in the Objective Structured Clinical Examination (OSCE).

OBJECTIVES

This study explores the following aims: i) to determine the effects of mood states on academic performance; ii) to assess the effect of mood states on self-efficacy; and iii) to analyze whether self-efficacy mediates the relationship between mood state and academic performance in the OSCE.

DESIGN

This quantitative and cross-sectional study received Institutional Review Board approval.

SETTINGS

A private university in Santiago, Chile.

PARTICIPANTS

A convenience sample of 117 nursing students from fourth and sixth semester.

METHODS

Data were collected using two questionnaires (the short version of the Profile of Mood State, and the General Self-Efficacy Scale), and the objective structured clinical examination in adult medical-surgical nursing and pediatric nursing courses.

RESULTS

Mood state has a significant effect on self-efficacy β = -0.068 p = .001. There was, however, no significant relationship between mood and academic performance (= - 0.004 p = .114). Similarly, Sobel's test indicated a non-significant mediation effect for self-efficacy-0.000544; 95 %; CI (-0.00256, 0.00148) p = .597.

CONCLUSIONS

The results of this study improve the understanding of students' self-efficacy and academic performance. Further investigation regarding the use of the self-efficacy construct as a mediating variable in different high-stakes evaluations and in students with varying training levels is needed.

Application of the cognitive load theory in prelicensure nursing education: a quantitative measurement focusing on instructional design

The objective of this study was to examine differences in principles from the cognitive load theory (CLT) combined with active learning strategies. To examine these differences, simple to complex activities were imbedded into two active learning instructional strategies: cooperative learning (CPL) and problem-based learning (PBL) and evaluated for its impact on mental effort for learning and intrinsic cognitive load. A two-within repeated-measures design was used in a nursing fundamentals classroom. The simple and complex activities were grounded in recommendations from the CLT and validated by nurse experts. The instructional strategies of CPL and PBL were chosen based on recommendations from the literature. Results revealed no statistically significant differences. A small interaction showed a decrease in mental effort and intrinsic load when problem-based learning was used. Progressing learning activities from simple to complex, informed by the CLT, offers an approach to designing instruction in nursing classrooms when using active learning strategies.

Anxiety is associated with extraneous cognitive load during teaching using high-fidelity clinical simulation

High-fidelity clinical simulation is currently a well-established teaching tool. However, high-fidelity representations of patients in critical conditions have the potential to elicit emotions among learners and impact their cognitive load (CL). Teaching with clinical simulation may induce both emotional and cognitive overloads. The relationship between anxiety and CL during clinical simulation was studied. Forty-one undergraduate medical students participated in this study; 19 males and 22 females. The state-anxiety component of State-Trait Anxiety Inventory was administered during clinical simulation teaching sessions at time points: pre-scenario, post-scenario and post-debriefing. The Cognitive Load Scale (Leppink et al.) questionnaire was also completed post-scenario. This assessed the three components of CL: intrinsic cognitive load (ICL), extraneous cognitive load (ECL) and self-perceived learning (SPL). Median CL scores for ICL, ECL and SPL were compared between groups of low-anxiety and high-anxiety participants using a Mann-Whitney U test. State-anxiety scores were high for both the pre-scenario and post-scenario time points with a significant reduction following post-debriefing. The median (interquartile range) state-anxiety scores were 41.0 (33.0-50.0), 46.0 (33.0-52.0) and 31.0 (23.0-39.0) for the pre-scenario, post-scenario and post-debriefing time points respectively. Students with high state-anxiety had higher ECL scores (median = 2.0) than students with low state-anxiety (median = 0.9) at the post scenario time point (U = 220, p = 0.043). No statistical relation was seen with state-anxiety for either ICL or SPL. State-anxiety immediately after the simulation scenario is associated with ECL but not ICL or SPL.

Effect of Psychophysiological Stress and Socio-Emotional Competencies on the Clinical Performance of Nursing Students during a Simulation Practice

Psychophysiological stress can affect the cognitive response and effective learning of students during medical simulation practices. This study aimed to explore the effect of psychophysiological stress and socio-emotional competencies on clinical performance during a simulation experience. A pre-test/post-test design was used to assess physiological (blood pressure, heart rate and blood oxygen saturation) and psychological parameters (stress and anxiety) as well as socio-emotional skills (cognitive load, self-efficacy and motivation) in nursing students (n = 40) before and after the simulation of a cardiopulmonary resuscitation practice. Physiological responses showed statistically significant differences between pre-test and post-test conditions for blood pressure and heart rate (p < 0.0001). Moderate and significant correlations were also observed when comparing self-efficacy with stress (r = −0.445, p = 0.004), anxiety (r = −0.467, p = 0.002) and motivation (r = −0.406, p = 0.009) measures. Similarly, cognitive-load dimensions were significantly associated with either physiological (r = −0.335, p = 0.034) or psychological (r = −0.448, p = 0.004) indicators. The analysis of multiple regression models revealed a relationship between the effectiveness of the simulated experience, post-test blood oxygen saturation, heart rate, workload and self-efficacy (R² = 0.490; F (3, 39) = 8.305; p < 0.0001; d = 1.663). Therefore, the evaluation of psychophysiological parameters and socio-emotional skills seems to provide a promising framework for predicting the quality of simulated clinical practices.

Recognition of cognitive load with a stacking network ensemble of denoising autoencoders and abstracted neurophysiological features

The safety of human-machine systems can be indirectly evaluated based on operator's cognitive load levels at each temporal instant. However, relevant features of cognitive states are hidden behind in multiple sources of cortical neural responses. In this study, we developed a novel neural network ensemble, SE-SDAE, based on stacked denoising autoencoders (SDAEs) which identify different levels of cognitive load by electroencephalography (EEG) signals. To improve the generalization capability of the ensemble framework, a stacking-based approach is adopted to fuse the abstracted EEG features from activations of deep-structured hidden layers. In particular, we also combine multiple K-nearest neighbor and naive Bayesian classifiers with SDAEs to generate a heterogeneous classification committee to enhance ensemble's diversity. Finally, we validate the proposed SE-SDAE by comparing its performance with mainstream pattern classifiers for cognitive load evaluation to show its effectiveness.