Background: The emergency department (ED) help desk is an undergraduate-run service learning program that screens ED patients for social needs, connects them to community resources, and follows-up to promote connections with resources. Students accepted to the program participate in a didactic course on the fundamentals of social emergency medicine as well as available community resources. Students also receive training around interviewing patients and use of screening software. Students commit to at least three quarters of service, during which they attend weekly team meetings.
Methods: This qualitative study explores the impact of this service learning experience for students. Current and former students were identified by the director of the program. Purposive and snowball sampling was used to select a sample of participants that participated in a semistructured interview. Our codebook was developed inductively using thematic analysis. Themes were presented and discussed with the entire research team for further analysis and refinement. Data collection and analysis used a constant comparative approach, and data collection ceased when saturation was achieved.
Results: Study participants consisted of current and former ED help desk student volunteers ( = 21). All participants believed that the ED help desk service learning experience prepared them for future careers by providing an experience that filled a gap in their education. We identified four main themes: (1) participants' perceived impact on patients, (2) learning from patients' experiences and differences, (3) appreciating patients' vulnerability and collaboratively addressing patients' needs, and (4) learning to navigate patients' social needs within the broader health care system.
Conclusions: Our ED help desk service learning program offers a unique experience for students to learn about patients' social needs, participate in meaningfully interactions with patients, and empower themselves and patients to work together as coproducers of patients' care.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9178399 | PMC |
| http://dx.doi.org/10.1002/aet2.10760 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Randomized controlled trials (RCTs) evaluating anti-cancer agents often lack generalizability to real-world oncology patients. Although restrictive eligibility criteria contribute to this issue, the role of selection bias related to prognostic risk remains unclear. In this study, we developed TrialTranslator, a framework designed to systematically evaluate the generalizability of RCTs for oncology therapies.
View Article and Find Full Text PDFPurpose Of Review: This review aims to evaluate the impact of artificial intelligence (AI) on cancer health equity, specifically investigating whether AI is addressing or widening disparities in cancer outcomes.
Recent Findings: Recent studies demonstrate significant advancements in AI, such as deep learning for cancer diagnosis and predictive analytics for personalized treatment, showing potential for improved precision in care. However, concerns persist about the performance of AI tools across diverse populations due to biased training data.
A review of deep learning for brain tumor analysis in MRI.
NPJ Precis Oncol
January 2025
Athinoula A. Martinos Center for Biomedical Imaging, 149 13th St, Charlestown, MA, 02129, USA.
Recent progress in deep learning (DL) is producing a new generation of tools across numerous clinical applications. Within the analysis of brain tumors in magnetic resonance imaging, DL finds applications in tumor segmentation, quantification, and classification. It facilitates objective and reproducible measurements crucial for diagnosis, treatment planning, and disease monitoring.
View Article and Find Full Text PDFNon-invasive blood glucose monitoring using PPG signals with various deep learning models and implementation using TinyML.
Sci Rep
January 2025
Department of Electrical Engineering, California Polytechnic State University, San Luis Obispo, California, USA.
Accurate and continuous blood glucose monitoring is essential for effective diabetes management, yet traditional finger pricking methods are often inconvenient and painful. To address this issue, photoplethysmography (PPG) presents a promising non-invasive alternative for estimating blood glucose levels. In this study, we propose an innovative 1-second signal segmentation method and evaluate the performance of three advanced deep learning models using a novel dataset to estimate blood glucose levels from PPG signals.
View Article and Find Full Text PDFUsing transformer-based models and social media posts for heat stroke detection.
Sci Rep
January 2025
Chubu Institute for Advanced Studies, Chubu University, Kasugai, Aichi, Japan.
Event-based surveillance is crucial for the early detection and rapid response to potential public health risks. In recent years, social networking services (SNS) have been recognized for their potential role in this domain. Previous studies have demonstrated the capacity of SNS posts for the early detection of health crises and affected individuals, including those related to infectious diseases.
View Article and Find Full Text PDFWant 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!