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Background: Health systems and organizations seeking to achieve learning healthcare system principles are increasingly relying on embedded research teams to optimize delivery of evidence-based, high-quality care that improves patient and staff experience alike. However, building organizational capacity to conduct and benefit from embedded research may be challenging in the absence of clearer guidance on career pathways and training, as well as strategies for managing and supporting this unique workforce.
Methods: In February 2018, 115 attendees from multiple agencies, institutions and professional societies participated in a conference to accelerate development of learning healthcare systems through embedded research. Workgroups engaged in structured brainstorming discussions of key domains; 21 diverse members focused on strengthening the embedded research community through more explicit development and support of multilevel career trajectories.
Results: Emphasis emerged on the need for training that goes beyond traditional curricula in rigorous scientific methods to include leadership, communication, and other organizational and business skills rarely offered in research training programs. These skills are required for effective engagement of multilevel stakeholders supporting evidence-based changes in routine care. Improving readiness of other stakeholders to effectively act on evidence was noted as equally crucial, as was creation of mid-career development opportunities for researchers and implementers.
Conclusions: Further development and support of the embedded research workforce will require explicit attention to novel training programs and support of researchers and the stakeholders in the systems they aim to improve.
Implications: Strategies for improving career entry and mastery of skills that foster effective multilevel stakeholder engagement hold promise for strengthening the embedded research community and their contributions to systematic improvements in health and health care.
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http://dx.doi.org/10.1016/j.hjdsi.2020.100479 | DOI Listing |
Health Expect
October 2025
Department of Social Work and Social Administration, The University of Hong Kong, Hong Kong, China.
Background: Serving as peer supporters in later life has been linked to a greater sense of purpose and meaning in life. How the wisdom of older adults could be leveraged to improve the implementation of peer support work, however, has rarely been considered. We aimed to examine the perspectives of peer supporters in this study, including the challenges they encountered in practice and the strategies they developed to navigate their roles.
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October 2025
School of Pharmacy, Newcastle University, Newcastle Upon Tyne, UK.
Introduction: There remains limited research exploring the experiences of informal carers from ethnically minoritised groups, particularly to illustrate perceptions of caring roles and challenges they may face to address unmet needs. While barriers such as language, cultural expectations and discrimination are acknowledged in wider literature, little is known about how these influence caregiving experiences or access to services in practice. This work seeks to better describe the barriers and facilitators impacting carers from ethnically minoritised groups, as well as illustrate possible influences of culture and carer identity affecting this under-researched population.
View Article and Find Full Text PDFDrugs Aging
September 2025
Dalla Lana School of Public Health, University of Toronto, V1 06, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada.
Background And Objectives: Older adults living with dementia are a heterogeneous group, which can make studying optimal medication management challenging. Unsupervised machine learning is a group of computing methods that rely on unlabeled data-that is, where the algorithm itself is discovering patterns without the need for researchers to label the data with a known outcome. These methods may help us to better understand complex prescribing patterns in this population.
View Article and Find Full Text PDFClin Nurs Res
September 2025
Chonnam National University College of Nursing, Donggu, Gwangju, South Korea.
The increasing prevalence of diabetes mellitus (DM) and patients' lack of self-management awareness have led to a decline in health-related quality of life (HRQoL). Studies identifying potential risk factors for HRQoL in DM patients and presenting generalized models are relatively scarce. The study aimed to develop and evaluate a machine learning (ML)-based model to predict the HRQoL in adult diabetic patients and to examine the important factors affecting HRQoL.
View Article and Find Full Text PDFClin Nurs Res
September 2025
Xuzhou Medical University, Jiangsu Province, China.
This study aimed to develop and validate a machine learning-based predictive model for assessing the risk of fear of childbirth in pregnant women during late pregnancy. A cross-sectional observational study was conducted from November 2022 to July 2023, involving 406 pregnant women. Six machine learning algorithms, including Lasso-assisted logistic regression (LR), random forest (RF), eXtreme Gradient Boosting (XGB), support vector machine (SVM), Bayesian network (BN), and k-nearest neighbors (KNN), were used to construct the models with 10-fold cross-validation.
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