To mark International Day of Women and Girls in Science, we’re spotlighting examples of research from Medical Teacher and MedEdPublish that puts women’s health, lived experience, and equity in health professions education at the forefront.
MedEdPublish
Beyond bikini medicine: An analysis of Sex- and Gender-Informed Medicine in a preclinical undergraduate medical education
This study performed an audit of the five courses of the Harvard Medical School pre-clinical curriculum that teach physiology and pathophysiology using case-based collaborative learning (CBCL).
Despite the expanding literature demonstrating widespread sex and gender differences across all organ systems, only 6.6% of the cases make any mention to sex and gender differences in biochemistry, anatomy, genetics, physiology, pathophysiology, or psychology.
Gender, hierarchy, and implicit bias: An interdisciplinary pilot simulation study
Interdisciplinary collaboration and team dynamics play critical roles in patient safety, especially in the management of airway emergencies. However, these interactions can be influenced by implicit biases, which are often heightened in emergency scenarios.
This interdisciplinary simulation study explores how gender bias and hierarchy influence team dynamics during a simulated airway emergency, offering valuable insights into how power structures and implicit assumptions can shape clinical interactions and outcomes.
Medical Teacher
Can storytelling of women’s lived experience enhance empathy in medical students? A pilot intervention study
Medical students participated in a storytelling intervention that had three components: listening to live or recorded stories from women with abnormal uterine bleeding, reflective writing, and a debriefing session.
This study found that storytelling has real potential to enhance students’ empathy and deepen their understanding of women’s lived health experiences—an essential component of compassionate, patient-centred care.
Advancing medical education in cervical cancer control with large language models for multiple-choice question generation
This research explores the feasibility of using large language models (LLMs) to generate multiple-choice questions for cervical cancer control education and compare them with those created by clinicians.
The findings showed that, with carefully engineered prompts, LLMs can produce questions comparable in quality to those written by clinicians, though human experts still performed better at higher cognitive levels.
