Interdisciplinary Research Training Center in Cancer Care Delivery

为癌症患者提供护理是一项高风险、大容量、高成本的工作。在美国 2016年，超过160万成年人被诊断为浸润性癌症，预计患病率 到2024年达到1900万美国人。美国国家医学科学院等机构已经提出了 在整个癌症护理过程中，关于患者接受的护理质量的问题一直存在。快速 如果癌症治疗方法不能在 在不同的治疗环境中，以安全、有效、高效、公平和以患者为中心的方式。给定 最优癌症护理服务面临的普遍挑战，需要更多的研究能力来加快 发现护理差距，开发和测试有效的干预措施，并将发现落实到日常工作中 临床实践。拟议中的培训将使来自不同学科的科学家做好准备，以促进这一需要 研究以及了解和改进癌症护理服务，从而填补癌症研究中的一个关键空白。 这一新的T32应用程序的目标是在癌症护理提供方面进行跨学科研究培训，目的是 培养3名博士后和14名博士后，成为从事癌症护理的独立科学家 三个重点领域的交付研究：1)描述性发现；2)干预开发和 测试，以及3)实施科学。该计划将通过提供坚实的 在研究方法和相关理论方面的基础，在实质性科学领域的知识深度 与癌症护理提供相关的研究经验，并将社会化转化为规范 高质量、伦理敏感的研究行为。培训通过四个主要活动提供：辅导 研究经验，课程工作，选定的重点领域，专业发展和 社交。学员将有一个个性化的发展计划和一个由 在癌症护理提供方面拥有积极研究计划的初级导师和具有内容的次级导师 和/或方法方面的专门知识。六所顶尖的研究生院--工程学、护理学、医学、 药剂学、公共卫生和社会工作--22名小学教师和10名中学教师提供癌症护理服务 研究专家承诺为学员提供指导。密歇根大学提供了一个杰出的 支持癌症护理提供研究培训的环境。主要机构资产包括罗杰尔 癌症中心、医疗政策和创新研究所、密歇根数据科学研究所以及 社会研究所。密歇根大学护理学院是排名前十的项目，将在 该方案并实施强有力的战略，以招募和留住不同的受训人员。 这一培训计划与国家癌症研究所和月球之蓝所宣布的优先事项一致 功能区小组建议通过最大限度地减少癌症治疗来减轻癌症负担 削弱副作用，扩大预防和早期检测，并挖掘数据以预测患者结果。

Care delivery for patients with cancer is a high-risk, high-volume, and high-cost endeavor. In the United States, over 1.6 million adults received new diagnoses of invasive cancer in 2016 and prevalence is expected to reach 19 million Americans by 2024. The National Academy of Medicine, among others, have raised persistent questions regarding the quality of care patients receive across the cancer care continuum. Rapid and impressive advances in cancer therapies may be rendered moot if such therapies cannot be given in a safe, effective, efficient, equitable, and patient-centered manner across diverse treatment settings. Given the pervasive challenges to optimal cancer care delivery, additional research capacity is needed to hasten the discovery of care gaps, develop and test efficacious interventions, and implement discoveries into routine clinical practice. The proposed training will prepare scientists from diverse disciplines to advance this needed research and to understand and improve cancer care delivery, thus filling a critical void in cancer research. The goal of this new T32 application, Interdisciplinary Research Training in Cancer Care Delivery, is to prepare three pre and 14 postdoctoral trainees to become independent scientists who conduct cancer care delivery research across three emphasis areas: 1) descriptive discovery; 2) intervention development and testing, and; 3) implementation science. The program will produce independent scientists by providing a solid foundation in research methods and pertinent theory, depth of knowledge in a substantive science area pertinent to cancer care delivery, experience in the conduct of research, and socialization into the normative behavior of high quality, ethically sensitive research. Training is provided through four main activities: mentored research experiences, course work, a selected emphasis area, and professional development and socialization. Trainees will have an individualized development plan and a mentorship team composed of a primary mentor with an active research program in cancer care delivery and a secondary mentor with content and/or methodological expertise. Across six top-ranked graduate schools – Engineering, Nursing, Medicine, Pharmacy, Public Health, and Social Work – 22 primary and 10 secondary faculty with cancer care delivery research expertise have pledged to mentor trainees. The University of Michigan provides an outstanding environment to support cancer care delivery research training. Key institutional assets include the Rogel Cancer Center, the Institute for Healthcare Policy and Innovation, the Michigan Institute for Data Science, and the Institute for Social Research. The University of Michigan School of Nursing, a top ten program, will house the program and implement robust strategies to recruit and retain a diverse pool of trainees. This training program aligns with stated priorities of the National Cancer Institute and the Moonshot Blue Ribbon Panel Recommendations to lessen the burden of cancer through minimizing cancer treatment’s debilitating side effects, expand prevention and early detection, and mine data to predict patient outcomes.