Investigating the Effectiveness of Implicit Bias Teaching Using High-Fidelity Simulation

Nursing education lacks explicit, effective teaching methods for recognizing and managing student biases, which could potentially impact patient outcomes and perpetuate health disparities. This study may influence changes in the associate degree nursing program curricula to include implicit bias teaching, which in turn helps nurses understand the concepts associated with health equity, such as health disparities. This quasi-quantitative experimental study aimed to investigate the effectiveness of implicit bias teaching using high-fidelity simulation to nursing students at a community college in the Pacific Northwest. A convenience sample of first-year nursing students was utilized in the study. Participants completed demographic surveys and the Implicit Association Test (IAT) as a pretest, traditional lectures on both groups, high-fidelity simulation for the treatment group, followed by the IAT posttest. The study is grounded in transformative learning theory and NLN Jeffries simulation theory to explore how high-fidelity simulation fosters critical reflection in addressing implicit bias among nursing students. A paired-sample t-test determined the mean in the pretest and posttest for the control (M = -0.0337, t (26) = -0.372, p = 0.713, with a 95% confidence interval of the mean difference ranging from -0.2202 to 0.1528), and the treatment group (M = -0.1511, t (26) = -1.666, p = 0.108, two-tailed, p = 0.054, one-tailed). High-fidelity simulation was not significantly more effective than traditional lectures in improving implicit bias scores. Limitations may include the intervention design, short study duration, or IAT sensitivity to capture subtle changes in implicit bias. Recommendations include larger, more diverse samples, longer interventions, refining simulation designs, and exploring alternative measurement tools.