Introduction: Our study examines how the professional and employment context may influence clinicians' practice self management support for patients with long term conditions (LTC).
Material And Methods: We surveyed clinicians working with patients with depression, chronic obstructive pulmonary disorder (COPD), chronic musculo skeletal pain and diabetes.
Results: Clinicians most frequently endorsed items on a scale concerned with patient centeredness, and less frequently endorsed items concerned with clinical and organizational self management support. The most important factors predicting these latter activities were the intensity of working experience with patients with LTC and attending professional training addressing the principles and practice of self management support. Practicing patient centeredness was endorsed by nearly all respondents, and so was not sensitive to variation on work variables.
Conclusions: The interaction of training and intensity of work with patients with LTC seems to have the most powerful effect on undertaking clinical and organizational self management support practices. To facilitate clinicians' practice of self management support for patients with LTC it is very important to provide relevant professional training and to build specialized patient care teams with professionals having complimentary skills.
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http://dx.doi.org/10.5114/aoms.2010.17100 | DOI Listing |
Plant Dis
January 2025
University of California Davis, Cooperative Extension, Napa, California, United States;
The timely detection of viral pathogens in vineyards is a critical aspect of management. Diagnostic methods can be labor-intensive and may require specialized training or facilities. The emergence of artificial intelligence (AI) has the potential to provide innovative solutions for disease detection but requires a significant volume of high-quality data as input.
View Article and Find Full Text PDFAnn Intern Med
January 2025
Clinical Epidemiology and Research Center (CERC), Department of Biomedical Sciences, Humanitas University, and IRCCS Humanitas Research Hospital, Milan, Italy, and Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Allergology and Immunology, Berlin, Germany (H.J.S.).
Description: Artificial intelligence (AI) has been defined by the High-Level Expert Group on AI of the European Commission as "systems that display intelligent behaviour by analysing their environment and taking actions-with some degree of autonomy-to achieve specific goals." Artificial intelligence has the potential to support guideline planning, development and adaptation, reporting, implementation, impact evaluation, certification, and appraisal of recommendations, which we will refer to as "guideline enterprise." Considering this potential, as well as the lack of guidance for the use of AI in guidelines, the Guidelines International Network (GIN) proposes a set of principles for the development and use of AI tools or processes to support the health guideline enterprise.
View Article and Find Full Text PDFJMIR Pediatr Parent
January 2025
Participatory eHealth and Health Data Research Group, Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden.
Background: With the increasing implementation of patient online record access (ORA), various approaches to access to minors' electronic health records have been adopted globally. In Sweden, the current regulatory framework restricts ORA for minors and their guardians when the minor is aged between 13 and 15 years. Families of adolescents with complex health care needs often desire health information to manage their child's care and involve them in their care.
View Article and Find Full Text PDFJMIR Form Res
January 2025
Department of Computer Science, University of California, Irvine, Irvine, CA, United States.
Background: Acute pain management is critical in postoperative care, especially in vulnerable patient populations that may be unable to self-report pain levels effectively. Current methods of pain assessment often rely on subjective patient reports or behavioral pain observation tools, which can lead to inconsistencies in pain management. Multimodal pain assessment, integrating physiological and behavioral data, presents an opportunity to create more objective and accurate pain measurement systems.
View Article and Find Full Text PDFJMIR Res Protoc
January 2025
Department of Public Health Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras, Malaysia.
Background: Postpartum depression remains a significant concern, posing substantial challenges to maternal well-being, infant health, and the mother-infant bond, particularly in the face of barriers to traditional support and interventions. Previous studies have shown that mobile health (mHealth) interventions offer an accessible means to facilitate early detection and management of mental health issues while at the same time promoting preventive care.
Objective: This study aims to evaluate the effectiveness of the Leveraging on Virtual Engagement for Maternal Understanding & Mood-enhancement (LoVE4MUM) mobile app, which was developed based on the principles of cognitive behavioral therapy and psychoeducation and serves as an intervention to prevent postpartum depression.
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