Objective: To explore predictors of optimism in parents of children with cancer.
Methods: A cross-sectional multi-centre study of 411 parents of children in active treatment for cancer was conducted. The Life Orientation Test-Revised was used to assess optimism. Other appropriate items and standardized questionnaires were used to assess parent and child characteristics. Predictors of optimism were explored using simple and multiple linear regression modelling techniques.
Results: The presence of positive intrapsychic traits, such as self-esteem and mastery, was more predictive of parental optimism than factors related to child cancer, such as the child's prognosis. Intrapsychic traits combined with an absence of parental depression, the parents' perception of the child's prognosis and parent education level predicted over 50% of the variance in parent optimism. Correlations between parents' and oncologists' view of the child's prognosis were low.
Conclusions: Positive intrapsychic traits are important predictors of optimism in the presence of a parent's positive view of the child's prognosis and higher education levels in the absence of depression. The results also favour the perspective of optimism as a trait of the parent who is resilient to a life stressor, such as dealing with childhood cancer. Additional knowledge about the role of optimism in caregiving for a child with cancer is needed before it can be explored for assessment or intervention purposes.
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http://dx.doi.org/10.1002/pon.1743 | DOI Listing |
Eur J Med Res
January 2025
Division of Radiology, Saraburi Hospital, Saraburi, Thailand.
Introduction: Stroke-associated pneumonia (SAP) is a major cause of mortality during the acute phase of stroke. The ADS score is widely used to predict SAP risk but does not include 24-h non-contrast computed tomography-Alberta Stroke Program Early CT Score (NCCT-ASPECTS) or red cell distribution width (RDW). We aim to evaluate the added prognostic value of incorporating 24-h NCCT-ASPECTS and RDW into the ADS score and to develop a novel prediction model for SAP following thrombolysis.
View Article and Find Full Text PDFBMC Cardiovasc Disord
January 2025
Center for Coronary Artery Disease, Division of Cardiology, Beijing Anzhen Hospital, Capital Medical University, 2 Anzhen Road, Chaoyang District, Beijing, 100029, China.
Background: Acute Kidney Injury (AKI) is a sudden and often reversible condition characterized by rapid kidney function reduction, posing significant risks to coronary artery disease (CAD) patients. This study focuses on developing accurate predictive models to improve the early detection and prognosis of AKI in CAD patients.
Methods: We used Electronic Health Records (EHRs) from a nationwide CAD registry including 54 429 patients.
Healthcare (Basel)
December 2024
Nursing Institute "Professor Radivoje Radić", Faculty of Dental Medicine and Health Osijek, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia.
Background/objectives: Numerous studies have examined nursing students' academic dishonesty; however, there is still a gap in understanding the predictors of such behavior. This study aimed to identify personal (intrapersonal and interpersonal) and contextual factors predicting nursing students' dishonesty during clinical training.
Methods: A two-phase, prospective, predictive study was conducted at a nursing faculty in Croatia.
BMC Geriatr
January 2025
Institute of Health Promotion and Sport Sciences, Faculty of Education and Psychology, ELTE Eötvös Loránd University, Bogdánfy St. 12, Budapest, H-1117, Hungary.
Background: Physical fitness and functioning are related to better mental health in older age. However, which fitness components (body composition, strength, flexibility, coordination, and endurance) are more closely related to psychological well-being (PWB) is unclear.
Methods: This research examined how body mass index (BMI) and six indices of functional fitness (i.
BMC Med Inform Decis Mak
December 2024
Division of Public Health Sciences, Department of Biostatistics and Data Science, Wake Forest University School of Medicine, 525 Vine St, Winston-Salem, NC, 27101, USA.
Background: A prediction model that estimates the risk of elevated glycated hemoglobin (HbA1c) was developed from electronic health record (EHR) data to identify adult patients at risk for prediabetes who may otherwise go undetected. We aimed to assess the internal performance of a new penalized regression model using the same EHR data and compare it to the previously developed stepdown approximation for predicting HbA1c ≥ 5.7%, the cut-off for prediabetes.
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