A Delphi Study for an AI Framework in Biomedical Education and Academics Researchers

Study Context Type of Study: Delphi method (iterative consensus-building process). Location: Spain. Language of Data: Spanish. Rounds: 2 rounds of questionnaires. Data Sources CSV Files from REDCap (3 files): File 1: Round1 Responses File 2: Closing remarks from questionnaire 1 File 3: Round 2 ResponsesContains updated ratings after feedback, plus any additional comments. Common Structure in CSVs: Columns: participant_id, item_id, score (Likert scale), comment (free text), timestamp. Rows: Each participant’s response per item. Codebooks (2 documents): Round 1 Codebook:Lists all items/questions presented in the first round, their identifiers, and definitions. Round 2 Codebook:Includes revised items based on Round 1 feedback, plus any new items added. Codebook Structure: Columns: item_id, item_text (in Spanish), domain (e.g., clinical, organizational), notes. Data Characteristics Quantitative Data:Likert-scale ratings (e.g., 1–9 for importance or agreement). Qualitative Data:Open-ended comments in Spanish, providing reasoning or suggestions. Panel Size:Number of participants (from demographic file). Iteration:Round 2 responses show convergence or divergence compared to Round 1. study funded by SEDEM (Spanish Society for Medical Education. Grant info: Becas-a-Proyectos-de-Investigacion-SEDEM-2023.pdf)