ArticleHeliyon2025
How to teach the anatomy of the inguinal canal? A multimodal approach.
Article in Heliyon, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Introduction: Learning the anatomy of the inguinal canal is challenging for medical students and residents. Current teaching via didactic lectures may not suffice for optimal learning, and visualization of the complicated 3D-structure of the inguinal canal. Hence, we investigated the efficacy of a low-fidelity model creation to improve the teaching. Materials and methods: 30 pre-clinical students were split into three groups and assigned different teaching interventions: 1) Lecture (Group L), 2) Lecture with individual model creation (Group M1), and 3) Lecture with group model creation (Group M2). Gain in knowledge were assessed through a pre- and post-intervention multiple-choice quizzes (MCQs), while perceptions of confidence/engagement/model efficacy were measured using post-intervention questionnaires. Results: For knowledge acquisition, average MCQ scores improved most for Group L (23 ± 1%), followed by M1 (19 ± 1%) and M2 (13 ± 1%). In learning the inguinal canal, perceived confidence was enhanced by modelling compared to lecture only (p < 0.05). There was no difference between group M1 and M2 respectively. Perceived engagement had also been enhanced by modelling compared to lecture only (p < 0.05). Interestingly, there was statistical difference between M1 and M2 (p < 0.05). However, perceived usefulness of the model compared to lecture only was insignificant (p > 0.05). There was also no difference between M1 and M2 (p > 0.05). Conclusion: As a supplement to didactic teaching of the inguinal canal, model creation was well-received, and provided opportunities for experiential learning. However, the usefulness of modelling in learning the anatomy and pathologies of inguinal canal was insignificant. Our research design was inadequate in showing long term gain. Additionally, results from group model creation could be affected by the dynamics amongst members. Future work should aim to address these issues.
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