| Sumario: | Background: Respiratory therapy training is an educational space that relies on clinical practice to assist respiratory therapists (RTs) in making split-second decisions in emergency situations. A large percentage of education programs in the field have turned to simulation training to address this critical training need. Research indicates that while respiratory therapy simulation training is effective, its implementation faces challenges such as high costs, limited faculty expertise, gaps in skill and content coverage, and the need for proper learner support. Decision-based learning (DBL) has shown potential in helping students work through the decision-making process of experts in a variety of academic fields and thus could provide an effective scaffold prior to trainees entering simulations or clinical practice. This study investigates the impact of DBL on RTs' decision-making abilities regarding a bronchiolitis high-flow nasal cannula algorithm along with subject matter experts' experiences developing decision models for use in training. Methods: A mixed-method study design was employed, utilizing pre- and postperformance tests to quantitatively assess the impact of DBL on decision-making abilities. In addition, qualitative interviews were conducted to gather in-depth insights from participants regarding their experiences with DBL. Results: The study found an improvement in test scores following DBL training, indicating its potential to enhance RTs' proficiency in protocol utilization and clinical decision-making. Collaborative experiences during the creation of DBL modules led to consensus among health care specialists and educators, improving protocol understanding and suggesting revisions for standardized care practices. Participants perceived DBL as filling a training gap by providing standardized practice and immediate feedback, although some expressed concerns about the method's perceived rigidity in decision-making. Conclusions: DBL shows promise as an educational tool in respiratory therapy, with potential to improve clinical decision-making and patient outcomes. However, the study also identifies areas for refinement in future implementations addressing concerns about flexibility in decision-making processes.
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