Learning Echocardiography in the Workplace: A Cognitive Load Perspective

Promoting physician-as-manager identities: The role of the workplace learning environment

Blissett and O'Sullivan contend that workplace learning environment contributes to learners devaluing administrative tasks and, as such, must be considered in strategies to promote a physician‐as‐manager identity.

Impact of delirium intervention on cognitive load among nurses in the intensive care unit: A multi-centre cluster randomized controlled trial

BACKGROUND

High cognitive load in nurses is a common problem in the intensive care unit (ICU). However, it remains unclear what different types of cognitive load the ICU nurses have experienced during the implementation of delirium interventions.

AIM

To describe the characteristics and explore the effect of implementing a delirium intervention on the cognitive load of nurses working in the ICU.

METHODS

A cluster-randomized controlled clinical trial was conducted. Six ICUs were randomized in a 1:1 ratio, and eligible nurses from these units provided either a delirium bundle intervention in addition to usual care (27 nurses) or usual care alone. An instrument was used to measure different types of cognitive load (MDT-CL), assessing intrinsic, extraneous and germane cognitive load. The repeated measures analysis of variance was used to detect between-group differences.

RESULTS

Among these nurses, significant between-group differences were identified in terms of their overall (P < 0.001), intrinsic (P < 0.001) and extraneous (P < 0.001) cognitive load. There was no significant change observed in the germane cognitive load (P = 0.489) in the delirium intervention group.

CONCLUSION

It is important to understand how the implementation of a delirium intervention affects different types of cognitive load in nurses, in order that tailored strategies can be applied to reduce cognitive load in ICU nurses.

Trainee selection of tasks in postgraduate medical education: Is there a role for 'cherry-picking' to optimise learning?

PURPOSE

Learning is optimised when postgraduate trainees engage in clinical tasks in their zone of proximal development (ZPD). However, workplace learning environments impose additional non-learning goals and additional tasks that may lead to trainees engaging in tasks that do not fall within their ZPD. We do not fully understand how trainees select clinical tasks in the workplace learning environment. If we knew the goals and factors they consider when selecting a task, we could better equip trainees with strategies to select tasks that maximise learning. We explored how postgraduate trainees select clinical tasks using echocardiography interpretation as a model.

METHODS

Canadian General Cardiology residents and Echocardiography fellows were invited to participate in semi-structured interviews. Aligning with a theory-informed study, two independent researchers used a deductive, directed content analysis approach to identify codes and themes.

RESULTS

Eleven trainees from seven Canadian universities participated (PGY4 = 4, PGY5 = 3, PGY6 = 1 and echocardiography fellows = 3). Goals included learning content, fulfilling assessment criteria and contributing to clinical demands. Trainees switched between goals throughout the day, as it was too effortful for them to engage in tasks within their ZPD at all times. When trainees had sufficient mental effort available, they selected higher complexity tasks that could advance learning content. When available mental effort was low, trainees selected less complex tasks that fulfilled numerically based assessment goals or contributed to clinical demands. Trainees predominantly used perceived complexity of the echocardiogram as a factor to select tasks to achieve their desired goals.

CONCLUSION

Postgraduate trainees select tasks within their ZPD that enable them to maximise learning when they perceive to have sufficient mental effort available and workplace affordances are adequate. These findings can inform individual and systemic strategies to maximise learning when selecting tasks.

Cognitive Load Assessment Scales in Simulation: Validity Evidence for a Novel Measure of Cognitive Load Types

INTRODUCTION

Cognitive load (CL) theory provides a framework to inform simulation instructional design. Reliable measures of CL types (intrinsic [IL], extraneous [EL], and germane load [GL]) in simulation are lacking. We developed the novel Cognitive Load Assessment Scales in Simulation (CLAS-Sim) and report validity evidence using Kane's framework.

METHODS

This quasi-experimental study tested the effect of a segmented/pause-and-debrief or standard/end-of-case-debrief intervention on pediatric residents' performance and self-rated CL in 2 complex- and simple-case simulations. After each simulation, participants completed 22 items measuring CL types. Three validity inferences were examined: scoring (instrument development and principal component analysis); generalization (internal consistency reliability of CL-component items across cases); and extrapolation [CLAS-Sim correlations with the single-item Paas scale, which measures overall CL; differences in primary task performance (high vs low); and discriminant validity of IL under different instructional-design conditions].

RESULTS

Seventy-four residents completed both simulations and postcase CLAS-Sim measures. The principal component analysis yielded 3 components: 4-item IL, 4-item EL, and 3-item GL scales (Cronbach's α, 0.68-0.77). The Paas scores correlated with CLAS-Sim IL and total CL scores in both cases ( rs range, 0.39-0.70; P ≤ 0.001). High complex-case performers reported lower IL and total CL (analyses of variance, each P < 0.001). In multivariate analyses of variance, CLAS-Sim IL, GL, and total CL varied across both cases by arm (each P ≤ 0.018); the segmented-debrief arm reported lower IL than the standard-debrief arm in both cases (each P ≤ 0.01).

CONCLUSIONS

The CLAS-Sim demonstrates preliminary validity evidence for distinguishing 3 CL types but requires further study to evaluate the impact of simulation-design elements on CL and learning.