Objectives: The aim of this study was to investigate which prepartum determinants contribute to the development of postpartum (PP) fatigue among working women in the Netherlands.
Methods: A prospective cohort study in 15 Dutch companies was conducted to measure different potential predictors using self-administrated questionnaires at baseline and at 30 weeks of pregnancy. Fatigue was measured at 12 (N=523) and 52 weeks (N=436) PP using the Checklist Individual Strength (CIS). A CIS score>76 was defined as fatigue.
Results: The prevalence of fatigue at 12 and 52 weeks PP was 24.5% and 18.1%, respectively. Fourteen predictive factors were found for fatigue (R(2)=0.37) at 12 weeks PP. Ten predictive factors were found for fatigue at 52 weeks PP (R(2)=0.36). In general, less favourable work relationships and characteristics, poorer mental health, more passive coping styles, more sleeping problems, more fatigue during pregnancy, and beliefs about child care arrangements were related to PP fatigue. At 30 weeks of pregnancy, only more fatigue (OR=3.69, p<0.001; OR=2.68, p=0.02) and poorer mental health (OR=0.50, p=0.02; OR=0.90, p=0.78) predicted fatigue both at 12 and 52 weeks PP.
Conclusions: A large number of predictive factors for PP fatigue were found. These findings indicate that different aspects can contribute to being fatigued after pregnancy. Further research is needed to investigate the effect of possible interventions by employers and/or occupational physicians.
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http://dx.doi.org/10.1016/j.jpsychores.2014.08.013 | DOI Listing |
Sci Rep
December 2024
School of Engineering and Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, Petaling Jaya, 47500, Selangor Darul Ehsan, Malaysia.
Cervical cancer is a deadly disease in women globally. There is a greater chance of getting rid of cervical cancer in case of earliest diagnosis. But for some patients, there is a chance of recurrence.
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December 2024
Imperial College London, London, UK.
Accurate estimation of the soil resilient modulus (M) is essential for designing and monitoring pavements. However, experimental methods tend to be time-consuming and costly; regression equations and constitutive models usually have limited applications, while the predictive accuracy of some machine learning studies still has room for improvement. To forecast M efficiently and accurately, a new model named black-winged kite algorithm-extreme gradient boosting (BKA-XGBOOST) is proposed.
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December 2024
Department of Applied Mathematics, Faculty of Mathematical Science, Ferdowsi University of Mashhad, Mashhad, Iran.
This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. After preprocessing and feature engineering, the Random Forest algorithm was identified as the best-performing model, achieving 99% accuracy for HTN prediction and 97% for CVD, outperforming other algorithms such as Logistic Regression and Support Vector Machines.
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December 2024
Department of Medical and Surgical Sciences, Institute of Cardiology, University of Bologna, Policlinico S.Orsola-Malpighi, via Massarenti 9, Bologna, 40138, Italy.
Cardiac implantable electronic devices infections (CIEDI) are associated with poor survival despite the improvement in transvenous lead extraction (TLE). Aetiology and systemic involvement are driving factors of clinical outcomes. The aim of this study was to explore their contribute on overall mortality.
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December 2024
School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Per- and polyfluoroalkyl substances (PFASs) have recently garnered considerable concerns regarding their impacts on human and ecological health. Despite the important roles of polyamide membranes in remediating PFASs-contaminated water, the governing factors influencing PFAS transport across these membranes remain elusive. In this study, we investigate PFAS rejection by polyamide membranes using two machine learning (ML) models, namely XGBoost and multimodal transformer models.
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