Abstract
Background Pharmacology is a crucial component of medications administration in nursing, yet nursing students generally find it difficult and self-rate their pharmacology skills as low. Objectives To evaluate nursing students learning pharmacology with the Pharmacology Inter-Leaved Learning-Cells environment, a novel approach to modeling biochemical interactions using a multiscale, computer-based model with a complexity perspective based on a small set of entities and simple rules. This environment represents molecules, organelles and cells to enhance the understanding of cellular processes, and combines these cells at a higher scale to obtain whole-body interactions. Participants Sophomore nursing students who learned the pharmacology of diabetes mellitus with the Pharmacology Inter-Leaved Learning-Cells environment (experimental group; n = 94) or via a lecture-based curriculum (comparison group; n = 54). Methods A quasi-experimental pre- and post-test design was conducted. The Pharmacology-Diabetes-Mellitus questionnaire and the course's final exam were used to evaluate students' knowledge of the pharmacology of diabetes mellitus. Results Conceptual learning was significantly higher for the experimental than for the comparison group for the course final exam scores (unpaired t = − 3.8, p < 0.001) and for the Pharmacology-Diabetes-Mellitus questionnaire (U = 942, p < 0.001). The largest effect size for the Pharmacology-Diabetes-Mellitus questionnaire was for the medication action subscale. Analysis of complex-systems component reasoning revealed a significant difference for micro-macro transitions between the levels (F(1, 82) = 6.9, p < 0.05). Conclusions Learning with complexity-based computerized models is highly effective and enhances the understanding of moving between micro and macro levels of the biochemical phenomena, this is then related to better understanding of medication actions. Moreover, the Pharmacology Inter-Leaved Learning-Cells approach provides a more general reasoning scheme for biochemical processes, which enhances pharmacology learning beyond the specific topic learned. The present study implies that deeper understanding of pharmacology will support nursing students' clinical decisions and empower their proficiency in medications administration.
Original language | English |
---|---|
Pages (from-to) | 175-181 |
Number of pages | 7 |
Journal | Nurse Education Today |
Volume | 61 |
DOIs | |
State | Published - 1 Feb 2018 |
Keywords
- Complex systems
- Computerized models
- Model-based learning
- Nursing education
- Pharmacology
All Science Journal Classification (ASJC) codes
- Education
- General Nursing