Is the Cognitive Complexity of Commitment‐to‐Change Statements Associated With Change in Clinical Practice? An Application of Bloom's Taxonomy

INTRODUCTION: This study categorizes 4 practice change options, including commitment-to-change (CTC) statements using Bloom's taxonomy to explore the relationship between a hierarchy of CTC statements and implementation of changes in practice. Our hypothesis was that deeper learning would be positively associated with implementation of planned practice changes. METHODS: Thirty-five family physicians were recruited from existing practice-based small learning groups. They were asked to use their usual small-group process while exploring an educational module on peripheral neuropathy. Part of this process included the completion of a practice reflection tool (PRT) that incorporates CTC statements containing a broader set of practice change options-considering change, confirmation of practice, and not convinced a change is needed ("enhanced" CTC). The statements were categorized using Bloom's taxonomy and then compared to reported practice implementation after 3 months. RESULTS: Nearly all participants made a CTC statement and successful practice implementation at 3 months. By using the "enhanced" CTC options, additional components that contribute to practice change were captured. Unanticipated changes accounted for one-third of all successful changes. Categorizing statements on the PRT using Bloom's taxonomy highlighted the progression from knowledge/comprehension to application/analysis to synthesis/evaluation. All PRT statements were classified in the upper 2 levels of the taxonomy, and these higher-level (deep learning) statements were related to higher levels of practice implementation. CONCLUSION: The "enhanced" CTC options captured changes that would not otherwise be identified and may be worthy of further exploration in other CME activities. Using Bloom's taxonomy to code the PRT statements proved useful in highlighting the progression through increasing levels of cognitive complexity-reflecting deep learning.