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Background: Globally, healthcare systems are experiencing a workforce crisis which has been exacerbated by the COVID19 pandemic. Numerous reports have documented the deterioration of healthcare professional wellbeing with burnout being called the new pandemic. Rehabilitation Medicine Physicians are among the most likely specialties to experience burnout.

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Background: While most Danish citizens never or very rarely call the national emergency helpline, 1-1-2, a few citizens call very often. In this article, we attend to the often-unheard voices of frequent callers, exploring why these citizens call 1-1-2 and why they often do not feel helped.

Methods: The article is based on a mixed-methods study on citizens in the Central Denmark Region who had called 1-1-2 five or more times during a period of six months in 2023.

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Introduction: The COVID-19 pandemic forced leaders and employees in health care services to take difficult decisions to manage risks associated with employee health and the organizations' functioning. This study aims to identify the changes in employee working routines, job demands, and job resources within Swedish maternal healthcare during the COVID-19 pandemic, and how these changes affected workload and health.

Methods: Data were derived from the longitudinal COPE Staff study involving midwives and physicians within maternal healthcare.

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Background: While an extensive body of research in palliative care exists on the experiences of grief and bereavement among family caregivers, much of this research is based on normative assumptions of who family caregivers are - housed, financially stable, and with extended family and/or friends to draw on for support. Research shows that in contexts of social disadvantage(e.g.

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Background: Fetal growth restriction (FGR) is a leading risk factor for stillbirth, yet the diagnosis of FGR confers considerable prognostic uncertainty, as most infants with FGR do not experience any morbidity. Our objective was to use data from a large, deeply phenotyped observational obstetric cohort to develop a probabilistic graphical model (PGM), a type of "explainable artificial intelligence (AI)", as a potential framework to better understand how interrelated variables contribute to perinatal morbidity risk in FGR.

Methods: Using data from 9,558 pregnancies delivered at ≥ 20 weeks with available outcome data, we derived and validated a PGM using randomly selected sub-cohorts of 80% (n = 7645) and 20% (n = 1,912), respectively, to discriminate cases of FGR resulting in composite perinatal morbidity from those that did not.

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