Objective: Excessive gestational weight gain (GWG) affects perinatal outcomes. However, to our knowledge, there are no studies on midwives’ knowledge and health guidance practices regarding GWG in Mongolia. Therefore, this study aimed to investigate midwives’ knowledge about weight control, clarify GWG guidance during pregnancy, and identify factors related to the implementation of GWG guidance in Mongolia.
Methods: A cross-sectional study using a web-based questionnaire was conducted among midwives registered with the Mongolian Midwives Association. The survey was conducted between July and August 2024. The questionnaire asked about calculating body mass index (BMI), BMI categories, and implementation of GWG health counseling. Multiple logistic regression analyses were conducted to identify factors associated with health guidance on GWG and BMI knowledge.
Results: A total of 414 responses were analyzed. Of the participants, 38.4% could correctly calculate BMI, and 37.7% could correctly answer what the BMI “normal weight” category was. Furthermore, 38.6% to 77.8% of midwives provided the 10 recommended health guidance items to more than 90% of pregnant women. Midwives working in hospital wards, those with fewer years of clinical experience, those who did not know how to calculate BMI, and those who did not have experience learning about weight control were associated with lower GWG health guidance scores.
Conclusions: This study clarified the health guidance practices regarding GWG and determined midwives’ level of knowledge about weight control in Mongolia. Postgraduate education for midwives should be enhanced to improve health guidance for GWG in Mongolia.
Artificial intelligence (AI) is transforming both higher education and healthcare practice landscapes, driving key stakeholders to explore the opportunities and challenges it offers. This paper presents real-world cases identified by a team of authors, including a graduate nursing faculty member, an occupational therapy faculty member and health informatics professional, a computer science faculty member, and a physician and bioethicist. The authors describe use cases of students who used AI in a graduate nursing course and examine the ethical implications. They share their current interprofessional perspectives and reflections to foster continued dialogue and discernment about the role of generative AI in higher education. As AI continues to grow and evolve, both educators and students will need to develop and understand ground rules. Ethics provides us with a way of exploring AI use cases so that we can share and learn from our own and others’ experiences.
Background: Healthcare disparities persist, resulting in inequities in access, quality, and health outcomes across populations. Addressing these factors through targeted strategies is essential for fostering equitable healthcare. Exploring student nurses’ perceptions of the root causes of health inequities throughout their nursing education is vital to training nurses to better treat their patients, both current and future.
Methods: A mixed-methods survey explored student nurses’ perceptions of the root causes of healthcare disparities, focusing on their awareness, attitudes, and beliefs. This cross-sectional study spanned one academic year and involved student nurses at various stages of their undergraduate education.
Results: Student nurses identify socioeconomic status, limited access to healthcare, and lack of health insurance coverage as the primary contributors to healthcare disparities. Findings inform that curricular enhancements in structural competency, resource navigation, and advocacy preparation are needed to strengthen readiness for equity-oriented nursing practice.
Conclusions: Integrating comprehensive Social Determinants of Health education into nursing curricula can better prepare future nurses for improved care delivery and reduce biases in healthcare settings.