A distributed learning strategy improves performance and retention of skills in neonatal resuscitation

Abstract

Skill retention after neonatal resuscitation training is poor. A distributed learning strategy – where learning is spread over multiple sessions – can improve retention of declarative memory (facts & knowledge). Session timings are critical – maximal retention occurs when a refresher session is scheduled at 10-30% of the time between initial training and test. We hypothesized this also holds true for neonatal resuscitation, a complex skill set requiring both declarative and procedural memory. We conducted a prospective, single-blinded randomized-controlled trial. University of Ottawa residents were recruited to training in neonatal resuscitation, with a high-fidelity simulated pre-test, immediate post-tests, and a retention test at 4 months. After training, they were randomized to either a refresher session at 3 weeks (18% of interval) or at 2 months (50%). Technical and non-technical skills were scored using validated checklists, knowledge with standardized questions. There was no difference between groups prior to the retention test. The early refresher group demonstrated significantly improved technical (mean ± 95% CI: 22.4 ± 1.3 v 18.2 ± 2.5, p = 0.02) and non-technical (31.0 ± 0.9 v 25.6 ± 3.1, p = 0.03) skill scores in the retention post-test compared to the late group. No difference was seen with knowledge scores. We conclude that both technical and non-technical aspects of neonatal resuscitation performance can benefit from an early refresher session. Session timings are critical and should be tailored to the desired length of skill retention. Findings may be generalizable to other interventions that depend on mixed types of memory.

Keywords

• Resuscitation
• High Fidelity Simulation Training
• Learning
• Pediatrics
• Neonatology