Resident Unions: Why Now and Will They Change Medical Education and Health Care?

Acad Med

B. Javed was assistant professor of clinical sciences education, A.T. Still University School of Osteopathic Medicine, Mesa, Arizona. She is now assistant dean of clerkship education, Michigan State University College of Osteopathic Medicine, Detroit, Michigan.

Published: October 2024

During the COVID-19 pandemic, resident unions proliferated. While unionization resulted in increased compensation and benefits, the process of union negotiations may have created adversarial relationships between residents and their institutions' leadership, who residents depend on for supervision and the development of clinical expertise. Such adversarial relationships could affect the learning environment, which is critical to the delivery of high-quality care. In this commentary, the authors suggest that academic medical centers should offer residents an authentic seat at the institutional care delivery leadership table, ensuring residents' full participation in key organizational decisions. Doing so represents an alternative to unionization, with its potentially adversarial relationships, while still achieving a key goal of residents-to be included in the decisions that affect them and the care they provide. In this way, residents can use their unique understanding of the institutions' strengths and weaknesses to improve the quality of patient care and the learning environment. Such engagement can also help residents achieve competence in systems-based practice and provide a vital link between institutions and the patients and community they serve through health policy and advocacy activities.

Download full-text PDF

Source
http://dx.doi.org/10.1097/ACM.0000000000005902DOI Listing

Publication Analysis

Top Keywords

adversarial relationships
12
resident unions
8
learning environment
8
residents
5
unions will
4
will change
4
change medical
4
medical education
4
education health
4
health care?
4

Similar Publications

Diffusion models, variational autoencoders, and generative adversarial networks (GANs) are three common types of generative artificial intelligence models for image generation. Among these, GANs are the most frequently used for medical image generation and are often employed for data augmentation in various studies. However, due to the adversarial nature of GANs, where the generator and discriminator compete against each other, the training process can sometimes end with the model unable to generate meaningful images or even producing noise.

View Article and Find Full Text PDF

The disease affects the optic nerve and represents the principle reasons of irreversible vision loss, mostly asymptomatic and uncontrolled. Consequently, early and accurate diagnosis is critical to prevent or reduce its effect, however, conventional diagnostic techniques often fail to provide concrete results. In this regard, we present a new approach built on Generative Adversarial Networks (GAN) and MobileNetV2 pretrained architecture for diagnosing glaucoma.

View Article and Find Full Text PDF

In order to promote the digital dissemination and preservation of Chinese intangible cultural heritage, this work constructs a digital platform for its transmission. The platform integrates a range of advanced technologies, including the Densely Connected Convolutional Networks - Bottleneck and Compression model, a notable convolutional neural network, along with natural language processing algorithms, generative adversarial network algorithms, and neural collaborative filtering algorithms. The platform is validated with 224,055 publicly archived valid data records, ensuring its effectiveness and reliability.

View Article and Find Full Text PDF

PADS-Net: GAN-based radiomics using multi-task network of denoising and segmentation for ultrasonic diagnosis of Parkinson disease.

Comput Med Imaging Graph

January 2025

The SMART (Smart Medicine and AI-based Radiology Technology) Lab, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China; Key Laboratory of Specialty Fiber Optics and Optical Access Networks, School of Communication and Information Engineering, Shanghai University, Shanghai, China. Electronic address:

Parkinson disease (PD) is a prevalent neurodegenerative disorder, and its accurate diagnosis is crucial for timely intervention. We propose the PArkinson disease Denoising and Segmentation Network (PADS-Net), to simultaneously denoise and segment transcranial ultrasound images of midbrain for accurate PD diagnosis. The PADS-Net is built upon generative adversarial networks and incorporates a multi-task deep learning framework aimed at optimizing the tasks of denoising and segmentation for ultrasound images.

View Article and Find Full Text PDF

Contrastive Graph Representation Learning with Adversarial Cross-View Reconstruction and Information Bottleneck.

Neural Netw

January 2025

School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; Ministry of Education Key Laboratory for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an, 710049, China. Electronic address:

Graph Neural Networks (GNNs) have received extensive research attention due to their powerful information aggregation capabilities. Despite the success of GNNs, most of them suffer from the popularity bias issue in a graph caused by a small number of popular categories. Additionally, real graph datasets always contain incorrect node labels, which hinders GNNs from learning effective node representations.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!