Background: Archetypes are representations of a group of people with shared behaviors, attitudes, and characteristics. The design and use of archetypes have potential application to increase partnership and support when embedding and scaling interventions but methodological approaches have not been developed.
Objective: To describe the methodology of designing archetypes for use in a pragmatic trial of advance care planning in the primary care context, SHARING Choices ((NCT04819191). We present resulting archetypes representing three key roles (primary care champion, advance care planning facilitator, and patient) in our pragmatic trial.
Methods: Our process for developing archetypes involved 4 steps: 1) Identify roles for archetype development, 2) Identify Shareholders and Data Sources for Archetype Development, 3) Generate unique archetypes and their distinguishing traits, and 4) Iteratively refine archetypes through exposure, scrutiny, and shareholder input. We also developed a process map to communicate our methodology.
Results: We created 6 distinct archetypes for the primary care champion role, 5 archetypes for the advance care planning facilitator role and 6 archetypes for the patient role. For each archetype we described strengths, challenges, prevailing emotions, and successful approaches to collaboration (e.g., "what works for me"). Unique opportunities for synergy between archetypes (such as with facilitator and champion) and potential challenges between archetypes (such as for facilitator and patient) suggest ways to improve training and support of key roles.
Discussion: Our process for creating archetypes for use in implementation research was iterative and informative in discussion of implementation with shareholders. We expect this methodology to be useful for anticipating and analyzing many aspects of implementation.
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http://dx.doi.org/10.1186/s43058-024-00683-9 | DOI Listing |
Neurospine
December 2024
Department of Neurosurgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Our research examines the learning curves of various minimally invasive lumbar surgeries to determine the benefits and challenges they pose to both surgeons and patients. The advent of microsurgical techniques since the 1960s, including advances in fluoroscopic navigation and intraoperative computed tomography, has significantly shifted spinal surgery from open to minimally invasive methods. This study critically evaluates surgical duration, intraoperative conversions to open surgery, and complications as primary parameters to gauge these learning curves.
View Article and Find Full Text PDFESMO Open
January 2025
Office of Quality and Value, The University of Texas MD Anderson Cancer Center, Houston, USA.
Many patients with cancer approaching the end of life (EOL) continue to receive treatments that are unlikely to provide meaningful clinical benefit, potentially causing more harm than good. This is called overtreatment at the EOL. Overtreatment harms patients by causing side-effects, increasing health care costs, delaying important discussions about and preparation for EOL care, and occasionally accelerating death.
View Article and Find Full Text PDFJ Pediatr Surg
December 2024
Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands.
Background And Aim: In this retrospective single center cohort study, we report the surgical outcomes of nephron-sparing surgery (NSS) for Wilms' tumor (WT) patients since centralization of pediatric oncology care in the Netherlands, and implementation of technological advancements. Therewith we describe the influence of experience and innovations for this type of surgery.
Methods: We retrospectively assessed all NSS procedures from January 1st 2015 until January 1st 2024 for patients who underwent surgery for a renal tumor at the Princess Máxima Center for Pediatric Oncology.
Acad Radiol
January 2025
Department of Radiology, Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL 33431 (M.R.). Electronic address:
Large Language Models (LLMs) such as ChatGPT have been increasingly integrated into radiology research, revolutionizing the research landscape. The Radiology Research Alliance (RRA) of the Association for Academic Radiology (AAR) has convened a Task Force to develop this guide to help radiology researchers responsibly adopt LLM technologies. LLMs can improve various phases of the research process by helping to automate literature reviews, generate research questions, analyze complex datasets, and draft manuscripts.
View Article and Find Full Text PDFSci Bull (Beijing)
December 2024
China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100029, China; National Center for Respiratory Medicine; State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing 100029, China. Electronic address:
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