A qualitative, community-based needs assessment to inform the development of a radiology health equity curriculum

IF 1.5 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Clinical Imaging Pub Date : 2025-02-08 DOI:10.1016/j.clinimag.2025.110431
Esteban A. Barreto , Alexander W. Burns , Jerome Watts , Pooja Agrawal , Anirudh Sudarshan , Jerome Seglah , Ayub Khan , David Bowen , Pragya Dhar , Neha Udayakumar , Gezzer Ortega , Lucy B. Spalluto , Efrén J. Flores
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引用次数: 0

Abstract

Background

Recognizing barriers to care is crucial for radiologists to ensure equitable access and quality care for all patients. Health equity trainings have shown promising results in helping providers understand unique patient needs.

Purpose

To perform community-based focus groups to gather needs assessment data as the foundation for a health equity curriculum for radiology professionals.

Methods

We conducted focus groups to gather in-depth qualitative data regarding health inequities in radiology. Radiology residents, medical students, and community partners were recruited from two large academic medical centers, three medical schools, three radiology trainee programs, and five community organizations. Focus groups were conducted in-person and virtually from December 2022 to April 2023, recorded, and transcribed. Qualitative analysis with multiple coding of verbatim transcripts was performed by study staff, overseen by a senior analyst.

Results

Study participants included trainees (medical students, n = 19; and radiology residents, n = 6), and community partners (n = 14) across 4 focus groups. Three major themes emerged from our discussions: (1) Awareness of Healthcare Inequities, (2) A Gap Between Knowledge and Practice, and (3) Achieving Equity in Radiology Care. Trainees highlighted current gaps in education while expressing a desire to receive applied learning experiences to prevent gaps from widening. Community partners shared difficult experiences and expressed a desire to be heard.

Conclusion

Health equity training can help future radiologists better understand diverse patient needs. Fostering community partnerships can ensure that the voices of diverse communities guide the development of health equity curricula in radiology.
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进行以社区为基础的定性需求评估,为制定放射学保健公平课程提供信息
认识到护理障碍对于放射科医生确保所有患者公平获得优质护理至关重要。保健公平培训在帮助提供者了解患者的独特需求方面显示出良好的效果。目的开展以社区为基础的焦点小组,收集需求评估数据,为放射科专业人员健康公平课程奠定基础。方法开展焦点小组调查,收集有关放射学卫生不公平的深入定性数据。研究人员从两个大型学术医疗中心、三所医学院、三个放射学培训项目和五个社区组织招募了住院医师、医学生和社区合作伙伴。焦点小组在2022年12月至2023年4月期间进行了面对面和虚拟的调查,并进行了记录和转录。在高级分析师的监督下,研究人员对逐字记录进行了多重编码的定性分析。结果研究对象包括实习生(医学生,n = 19;以及4个焦点小组的放射科住院医师(n = 6)和社区合作伙伴(n = 14)。我们的讨论中出现了三个主要主题:(1)对医疗不公平的认识,(2)知识与实践之间的差距,(3)实现放射学护理的公平。学员们强调了目前的教育差距,同时表示希望获得实用的学习经验,以防止差距扩大。社区伙伴们分享了艰难的经历,并表达了希望被倾听的愿望。结论健康公平培训可以帮助未来放射科医师更好地了解不同患者的需求。促进社区伙伴关系可确保不同社区的声音指导放射学保健公平课程的发展。
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来源期刊
Clinical Imaging
Clinical Imaging 医学-核医学
CiteScore
4.60
自引率
0.00%
发文量
265
审稿时长
35 days
期刊介绍: The mission of Clinical Imaging is to publish, in a timely manner, the very best radiology research from the United States and around the world with special attention to the impact of medical imaging on patient care. The journal''s publications cover all imaging modalities, radiology issues related to patients, policy and practice improvements, and clinically-oriented imaging physics and informatics. The journal is a valuable resource for practicing radiologists, radiologists-in-training and other clinicians with an interest in imaging. Papers are carefully peer-reviewed and selected by our experienced subject editors who are leading experts spanning the range of imaging sub-specialties, which include: -Body Imaging- Breast Imaging- Cardiothoracic Imaging- Imaging Physics and Informatics- Molecular Imaging and Nuclear Medicine- Musculoskeletal and Emergency Imaging- Neuroradiology- Practice, Policy & Education- Pediatric Imaging- Vascular and Interventional Radiology
期刊最新文献
Contents Comment on "Limited performance of ChatGPT-4v and ChatGPT-4o in image-based core radiology cases". Comment on “Classifying the clinical significance of common breast pain symptoms using a large language model, ChatGPT (GPT-4)” Comment on “Diagnostic accuracy and limitations of intravoxel incoherent motion diffusion-weighted imaging for differentiating breast tumors: A systematic review and meta-analysis” Comment on 'Limited performance of ChatGPT-4v and ChatGPT-4o in image-based core radiology cases'.
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