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Voices on academic accreditation: lived experiences of nurse educators, administrators, students, and alumni in nursing education.
BMC Med Educ
January 2025
College of Nursing - Jeddah, King Saud Bin Abdul-Aziz University for Health Sciences, P.O.Box.9515, Jeddah, 21423, Saudi Arabia.
Background: Academic accreditation is a pivotal process in nursing education, ensuring program quality, consistency, and graduate readiness for professional practice. Despite its significance, limited research explores the lived experiences and perspectives of stakeholders, including academic nurse educators, administrators, students, and alumni-engaged in accreditation.
Purpose: This study aimed to explore the lived experiences, perceptions, and insights of nursing education stakeholders regarding the accreditation process, focusing on its impact on educational quality, program reputation, and professional preparation.
AI Interventions to Alleviate Healthcare Shortages and Enhance Work Conditions in Critical Care: Qualitative Analysis.
J Med Internet Res
January 2025
ETH Zurich, Zurich, Switzerland.
Background: The escalating global scarcity of skilled health care professionals is a critical concern, further exacerbated by rising stress levels and clinician burnout rates. Artificial intelligence (AI) has surfaced as a potential resource to alleviate these challenges. Nevertheless, it is not taken for granted that AI will inevitably augment human performance, as ill-designed systems may inadvertently impose new burdens on health care workers, and implementation may be challenging.
View Article and Find Full Text PDFUnited Nations, the Struggle for Gender Equity, and Queering Global Science.
OMICS
January 2025
OMICS: A Journal of Integrative Biology, New Rochelle, New York, USA.
UN Women is the United Nations "entity dedicated to gender equality and the empowerment of women". UN Women is an example of the institutions of global governance that followed the gender turn in women's rights over the past 2 decades. This opinion commentary unpacks a brief history of UN Women, and the ongoing disparities in gender diversity, equity, and inclusion (DEI) in science, engineering, and medicine, not to mention in science communication, with the aim to shed light on the adverse impacts of gender essentialism and gender binary.
View Article and Find Full Text PDFThe relationship between self-efficacy, learning burnout, willingness to fulfill the contract and empathy of rural-oriented tuition-waived medical students of China: a cross-sectional study.
BMC Med Educ
January 2025
School of Integrated Traditional Chinese and Western Medicine, Jining Medical University, Jining, 272067, China.
Background: The empathy of rural-oriented tuition-waived medical students (RTMSs) is closely related to the construction of good doctor-patient relationship and the quality of rural medical and health services. The purpose of this study is not only to explore the relationship between self-efficacy, learning burnout, willingness to fulfill the contract and empathy, but also to explore the mediating role of self-efficacy between learning burnout and empathy, and between willingness to fulfill the contract and empathy.
Methods: Four hundred ninety-five rural-oriented tuition-waived medical students from 3 medical universities in Shandong Province were selected as research subjects, and General self-efficacy scale (GSES), Learning burnout of university student (LBUS), Willingness to fulfill the contract scale and Jefferson Scale of Physician Empathy-student version (JSPE-S) were used to investigate.
Assessing chemical exposure risk in breastfeeding infants: An explainable machine learning model for human milk transfer prediction.
Ecotoxicol Environ Saf
January 2025
Department of Pharmacy, Jieyang People's Hospital, Jieyang, China.
Breast milk is essential for infant health, but the transfer of xenobiotic chemicals poses significant risks. Ethical challenges in clinical trials necessitate the use of in vitro predictive models to assess chemical exposure risks in breastfeeding infants. This study introduces an explainable machine learning model to predict the risk of chemical transfer through human milk.
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