Background: There is an unquestionable need to adapt health care to the needs of each woman, to foster her self-confidence and provide her with the autonomy to manage her own maternity. This involves empowering her to choose and face her model of childbirth and childcare responsibly. The range of self-management health needs tests offered by the scientific community at this stage of life is practically non-existent. In this project, we intend to develop and evaluate the validity, reliability and ease of use of two self-administered analysis instruments for: 1.- Needs of women preparing for childbirth and 2.- Identification of alarm symptoms in the puerperium.
Methods: This is a descriptive study of the clinimetric characteristics and usability of two developed self-applied digital instruments for measuring needs in childbirth and postpartum based on the recommendations made in the consensus-based standards for the selection of health measurement instruments (COSMIN) and by the International Test Commission (ITC). The study consists of two phases: 1 - Evaluation of the clinimetric properties of the two instruments, which were developed and then altered, based on their comprehensibility and global usability estimated from a pilot study and 2 - Pre-implementation study.
Discussion: The final product will be two valid, reliable, usable instruments for self-assessment of health needs that are highly acceptable to young couples and the professionals who serve them. They will be a valuable resource for meeting the needs of the population more efficiently and guiding decision-making, and they will contribute to the greater sustainability of the health system.
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http://dx.doi.org/10.1186/s12884-020-03377-x | DOI Listing |
Sci Rep
December 2024
Department of Medical Microbiology, Radboudumc, Nijmegen, The Netherlands.
The aetiology of Alzheimer's disease (AD) and Parkinson's disease (PD) are unknown and tend to manifest at a late stage in life; even though these neurodegenerative diseases are caused by different affected proteins, they are both characterized by neuroinflammation. Links between bacterial and viral infection and AD/PD has been suggested in several studies, however, few have attempted to establish a link between fungal infection and AD/PD. In this study we adopted a nanopore-based sequencing approach to characterise the presence or absence of fungal genera in both human brain tissue and cerebrospinal fluid (CSF).
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December 2024
School of Physical Education, Southwest Petroleum University, Chengdu, 610500, China.
Stroke is one of the leading causes of death in developing countries, and China bears the largest global burden of stroke. This study aims to investigate the relationship between different dimensions of physical activity levels and stroke risk using a nationally representative database. We performed a cross-sectional analysis using data from the China Health and Retirement Longitudinal Study (CHARLS) 2020.
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December 2024
KAUST Center of Excellence for Smart Health (KCSH), King Abdullah University of Science and Technology, Thuwal, 23955, Saudi Arabia.
Analyzing microbial samples remains computationally challenging due to their diversity and complexity. The lack of robust de novo protein function prediction methods exacerbates the difficulty in deriving functional insights from these samples. Traditional prediction methods, dependent on homology and sequence similarity, often fail to predict functions for novel proteins and proteins without known homologs.
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December 2024
Department of Diagnostic Radiology, Dalhousie University, Halifax, Canada.
The goal of this study was to determine how radiologists' rating of image quality when using 0.5T Magnetic Resonance Imaging (MRI) compares to Computed Tomography (CT) for visualization of pathology and evaluation of specific anatomic regions within the paranasal sinuses. 42 patients with clinical CT scans opted to have a 0.
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December 2024
School of Mechanical Engineering, Liaoning Engineering Vocational College, Tieling, 112008, Liaoning, People's Republic of China.
The paper proposes a multi-rigid-body system state identification method based on self-healing model in order to improve the accuracy and reliability of CNC machine tools. Firstly, considering the influence of the joint surface, the Lagrange method is used to establish the mechanical model of the multi-rigid-body system. We input acceleration information and use the second-order modulation function to complete the online real-time identification of the joint surface parameters, thereby establishing the self-healing mechanical model of the multi-rigid-body system.
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