High-fidelity simulation versus case-based tutorial sessions for teaching pharmacology: Convergent mixed methods research investigating undergraduate medical students' performance and perception

Abstract

Introduction: Medical educators strive to improve their curricula to enhance the student learning experience. The use of high-fidelity simulation within basic and clinical medical science subjects has been one of these initiatives. However, there is paucity of evidence on using simulation for teaching pharmacology, especially in the Middle East and North Africa region, and the effectiveness of this teaching modality, relative to more traditional ones, have not been sufficiently investigated. Accordingly, this study compares the effects of high-fidelity simulation, which is designed in alignment with adult and experiential learning theories, and traditional case-based tutorial sessions on the performance and perception of undergraduate Year 2 medical students in pharmacology in Dubai, United Arab Emirates.

Methods: This study employed a convergent mixed methods approach. Forty-nine medical students were randomly assigned to one of two groups during the 16-week pharmacology course. Each group underwent one session delivered via high-fidelity simulation and another via a case-based tutorial. A short multiple-choice question quiz was administered twice (immediately upon completion of the respective sessions and 5 weeks afterwards) to assess knowledge retention. Furthermore, to explore the students' perceptions regarding the two modes of learning delivery (independently and in relation to each other), an evaluation survey was administered following the delivery of each session. Thereafter, the iterative joint display analysis was used to develop a holistic understanding of the effect of high-fidelity simulation in comparison to traditional case-based tutorial sessions on pharmacology learning in the context of the study.

Results: There was no statistically significant difference in students' knowledge retention between high-fidelity simulation and case-based tutorial sessions. Yet, students expressed a greater preference for high-fidelity simulation, describing the corresponding sessions as more varied, better at reinforcing learning, and closer to reality. As such, the meta-inferences led to expansion of the overall understanding around students' satisfaction, to both confirmation and expansion of the systemic viewpoint around students' preferences, and lastly to refinement in relation to the perspective around retained knowledge.

Conclusion: High-fidelity simulation was found to be as effective as case-based tutorial sessions in terms of students' retention of knowledge. Nonetheless, students demonstrated a greater preference for high-fidelity simulation. The study advocates caution in adapting high-fidelity simulation, where careful appraisal can lend itself to identifying contexts where it is most effective.

Fig 1. Timeline of the delivery of the two pharmacology topics: Routes of administration (ROA) and drug toxicity and interactions (TOX).

ROA was delivered to Student Group 1 via high-fidelity simulation and to Student Group 2 via a case-based tutorial during the fourth week of the course. TOX was delivered during the tenth week of the course to Student Group 1 via a case-based tutorial and to Student Group 2 via high-fidelity simulation. ICA, In-course assessment.

Fig 2. Data collection points superimposed on the timeline of the course delivery.

ROA, Routes of administration topic; TOX, drug toxicity and interactions topic; ST-Quiz, Short-term quiz; LT-Quiz, Long-term quiz; ICA, in-course assessment; Survey Quant., quantitative survey; Survey Qual., qualitative survey.

Fig 3. Mind map deployed as a tool to facilitate the qualitative analysis.

Fig 4. The study’s conceptual framework: Differentiators of pharmacology teaching modalities.

Fig 5. Bar graph illustrating the students’ preference and perceived usefulness of high-fidelity simulation versus case-based tutorial sessions for delivery of pharmacology teaching.

Fig 6. Output of the iterative joint display analysis process, resulting in three meta-inferences: Satisfaction, Preferences, and Retained Knowledge.

The secondary colour Purple emerged by mixing the primary colour Red with the primary colour Blue (which constitutes an analogy of the lateral and critical thinking that took place to generate the meta-inferences from the integration of two sets of primary inferences). For the ‘Satisfaction’ meta-inference, integrating the quantitative with the qualitative inferences led to an expansion in the overall viewpoint. For the ‘Preferences’ meta-inference, the integration led to both: confirmation and expansion of the overall viewpoint. As for the ‘Retained Knowledge’ meta-inference, the integration led to the refinement of the overall viewpoint.