Background: Health practitioners must be equipped with effective clinical reasoning skills to make appropriate, safe clinical decisions and avoid practice errors. Under-developed clinical reasoning skills have the potential to threaten patient safety and delay care or treatment, particularly in critical and acute care settings. Simulation-based education which incorporates post-simulation reflective learning conversations as a debriefing method is used to develop clinical reasoning skills while patient safety is maintained. However, due to the multidimensional nature of clinical reasoning, the potential risk of cognitive overload, and the varying use of analytic (hypothetical-deductive) and non-analytic (intuitive) clinical reasoning processes amongst senior and junior simulation participants, it is important to consider experience, competence, flow and amount of information, and case complexity related factors to optimize clinical reasoning while attending group- based post-simulation reflective learning conversations as a debriefing method. We aim to describe the development of a post-simulation reflective learning conversations model in which a number of contributing factors to achieve clinical reasoning optimization were addressed. Methods: A Co-design working group (N = 18) of doctors, nurses, researchers, educators, and patients’ representatives collaboratively worked through consecutive workshops to co-design a post-simulation reflective learning conversations model to be used for simulation debriefing. The co-design working group established the model through a theoretical and conceptual-driven process and multiphasic expert reviews. Concurrent integration of appreciative inquiry, plus/delta, and Bloom’s Taxonomy methods were considered to optimize simulation participants’ clinical reasoning while attending simulation activities. The face and content validity of the model were established using the Content Validity Index CVI and Content Validity Ratio CVR methods. Results: A Post-simulation reflective learning conversations model was developed and piloted. The model was supported with worked examples and scripted guidance. The face and content validity of the model were evaluated and confirmed. Conclusions: The newly co-designed model was established in consideration to different simulation participants’ seniority and competence, flow and amount of information, and simulation case complexity. These factors were considered to optimize clinical reasoning while attending group-based simulation activities.
- Reflective learning conversations model
- Post-simulation debriefing
- Clinical reasoning