Data and analysis code for: A Short Sex- and Gender-Based Medicine Lecture Series at a Women's Medical College in Korea: An Exploratory Pre-Post Pilot Study

This record contains the de-identified dataset, qualitative codebook, and analysis code underlying the study "A Short Sex- and Gender-Based Medicine Lecture Series at a Women's Medical College in Korea: An Exploratory Pre-Post Pilot Study," submitted to PLOS ONE. The study is a one-group pretest–posttest exploratory pilot evaluating a short sex- and gender-based medicine (SGBM) lecture series (n = 88 matched participants). Outcomes cover attitude, gender sensitivity, and clinical self-efficacy across 17 five-point Likert items in three domains. Files included: ESAC_SGBM_PLOS_Public_Deidentified_Dataset.xlsx — de-identified, paired item-level data (sheet "Paired_Item_Data"). The *_Score columns are analysis-ready; negatively worded items are already reverse-coded (6 − raw score). S2_File_Qualitative_Codebook_ESAC_SGBM.xlsx — codebook for the supplementary qualitative component. S3_File_Analysis_Code.py — Python script reproducing every quantitative result reported in the manuscript (Cronbach's alpha, paired-samples t-tests, Cohen's dz, Bonferroni and Benjamini–Hochberg FDR correction), including a built-in self-verification check. S3_reproduction_log.txt — plain-text log confirming that all reported values (30/30) reproduce from the raw data. Reproduction: run python S3_File_Analysis_Code.py ESAC_SGBM_PLOS_Public_Deidentified_Dataset.xlsx with Python 3.13.5 (pandas, scipy, numpy, openpyxl). A reproduction log is written to the working directory. Ethics: The study protocol was reviewed by the institutional review board and confirmed exempt (IRB No. EUMC 2026-06-057). The dataset contains no personal identifying information. The statistical analysis code was prepared with the assistance of a generative AI tool and was independently reviewed, executed, and verified by the authors, who take full responsibility for the accuracy of all reported results.