Evaluating Clinical Simulations for Learning Procedural Skills: A Theory-Based Approach

Abstract

Simulation-based learning is becoming widely established within medical education. It offers obvious benefits to novices learning invasive procedural skills, especially in a climate of decreasing clinical exposure. However, simulations are often accepted uncritically, with undue emphasis being placed on technological sophistication at the expense of theory-based design. The author proposes four key areas that underpin simulation-based learning, and summarizes the theoretical grounding for each. These are (1) gaining technical proficiency (psychomotor skills and learning theory, the importance of repeated practice and regular reinforcement), (2) the place of expert assistance (a Vygotskian interpretation of tutor support, where assistance is tailored to each learner's needs), (3) learning within a professional context (situated learning and contemporary apprenticeship theory), and (4) the affective component of learning (the effect of emotion on learning). The author then offers four criteria for critically evaluating new or existing simulations, based on the theoretical framework outlined above. These are: (1) Simulations should allow for sustained, deliberate practice within a safe environment, ensuring that recently-acquired skills are consolidated within a defined curriculum which assures regular reinforcement; (2) simulations should provide access to expert tutors when appropriate, ensuring that such support fades when no longer needed; (3) simulations should map onto real-life clinical experience, ensuring that learning supports the experience gained within communities of actual practice; and (4) simulation-based learning environments should provide a supportive, motivational, and learner-centered milieu which is conducive to learning.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 434 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.