20 Jahre Forschung zu Burnout und anderen Belastungsindikatoren bei Schweizer Ärztinnen und Ärzten

Aufgrund der hohen psychischen Belastung der Ärztinnen und Ärzte («Burnout-Epidemie») schlugen Expertinnen und Experten mehrfach Alarm. Wie ist die Lage hierzulande? In diesem Artikel wird die Forschung in der Schweiz (2000–2022) zusammengefasst, in den internationalen Wissensstand eingeordnet und kritisch bewertet

Physician-Specific Symptoms of Burnout Compared to a Non-Physicians Group

Physician burnout is a systemic problem in health care due to its high prevalence and its negative impact on professional functioning and individual well-being. While unique aspects of the physician role contributing to the development burnout have been investigated recently, it is currently unclear whether burnout manifests differently in physicians compared to the non-physician working population. We conducted an individual symptom analysis of burnout symptoms comparing a large sample of physicians with a non-physician group. In this cross-sectional online study, burnout was assessed with the Maslach Burnout Inventory—General Survey. We matched physicians with non-physicians regarding their age, gender, educational level, occupational status, and total burnout level using a “nearest neighbour matching” procedure. We then conducted a series of between-groups comparisons. Data of 3846 (51.0% women) participants including 641 physicians and 3205 non-physicians were analysed. The most pronounced difference was that physicians were more satisfied with their work performance (medium effect size (r = 0.343). Our findings indicate minor yet significant differences in burnout phenomenology between physicians and non-physicians. This demonstrates unique aspects of physician burnout and implies that such differences should be considered in occupational research among physicians, particularly when developing burnout prevention programs for physicians

Emotional Reactivity, Emotion Regulation Capacity, and Posttraumatic Stress Disorder in Traumatized Refugees: An Experimental Investigation

Refugees who suffer from posttraumatic stress disorder (PTSD) often react with strong emotions when confronted with trauma reminders. In this study, we aimed to investigate the associations between low emotion regulation capacity (as indexed by low heart rate variability [HRV]), probable PTSD diagnosis, and fear and anger reaction and recovery to trauma-related stimuli. Participants were 81 trauma-exposed refugees (probable PTSD, n = 23; trauma-exposed controls, n = 58). The experiment comprised three 5-min phases: a resting phase (baseline); an exposition phase, during which participants were exposed to trauma-related images (stimulus); and another resting phase (recovery). We assessed HRV at baseline, and fear and anger were rated at the end of each phase. Linear mixed model analyses were used to investigate the associations between baseline HRV and probable DSM-5 PTSD diagnosis in influencing anger and fear responses both immediately after viewing trauma-related stimuli and at the end of the recovery phase. Compared to controls, participants with probable PTSD showed a greater increase in fear from baseline to stimulus presentation, d = 0.606. Compared to participants with low emotion regulation capacity, participants with high emotion regulation capacity showed a smaller reduction in anger from stimulus presentation to recovery, d = 0.548. Our findings indicated that following exposure to trauma-related stimuli, probable PTSD diagnosis predicted increased fear reactivity, and low emotion regulation capacity predicted decreased anger recovery. Impaired anger recovery following trauma reminders in the context of low emotion regulation capacity might contribute to the increased levels of anger found in postconflict samples

Changes in mental health among U.S. military veterans during the COVID-19 pandemic: A network analysis

Increases of symptoms of posttraumatic stress disorder (PTSD), anxiety and depression have been observed among individuals exposed to potentially traumatic events in the first months of the COVID-19 pandemic. Similarly, associations among different aspects of mental health, such as symptoms of PTSD and suicidal ideation, have also been documented. However, studies including an assessment prior to the onset and during the height of the pandemic are lacking. We investigated changes in symptoms of PTSD, depression, anxiety, suicidal ideation, and posttraumatic growth in a population-based sample of 1232 U.S. military veterans who experienced a potentially traumatic event during the first year of the pandemic. Symptoms were assessed prior to (fall/winter 2019) and one year into the pandemic (fall/winter 2020). We compared changes in symptom interrelations using network analysis, and assessed their associations with pandemic-related PTSD and posttraumatic growth symptoms. A subtle increase in psychopathological symptoms and a decrease in posttraumatic growth was observed one year into the pandemic. The peripandemic network was more densely connected, and pandemic-related PTSD symptoms were positively associated with age, anxiety, worst-event PTSD symptoms, and pandemic-related posttraumatic growth. Our findings highlight the resilience of veterans exposed to a potentially traumatic event during the first year of a pandemic. Similarly, the networks did not fundamentally change from prepandemic to one year into the pandemic. Despite this relative stability on a group level, individual reactions to potentially traumatic events could have varied substantially. Clinicians should individualize their assessments but be aware of the general resilience of most veterans

On the validity of the centrality hypothesis in cross-sectional between-subject networks of psychopathology

Background In the network approach to psychopathology, psychiatric disorders are considered networks of causally active symptoms (nodes), with node centrality hypothesized to reflect symptoms’ causal influence within a network. Accordingly, centrality measures have been used in numerous network-based cross-sectional studies to identify specific treatment targets, based on the assumption that deactivating highly central nodes would proliferate to other nodes in the network, thereby collapsing the network structure and alleviating the overall psychopathology (i.e., the centrality hypothesis). Methods Here, we summarize three types of evidence pertaining to the centrality hypothesis in psychopathology. First, we discuss the validity of the theoretical assumptions underlying the centrality hypothesis in psychopathology. We then summarize the methodological aspects of extant studies using centrality measures as predictors of symptom change following treatment, while delineating their main findings and several of their limitations. Finally, using a specific dataset of 710 treatment-seeking patients with posttraumatic stress disorder (PTSD) as an example, we empirically examine node centrality as a predictor of therapeutic change, replicating the approach taken by previous studies, while addressing some of their limitations. Specifically, we investigated whether three pre-treatment centrality indices (strength, predictability, and expected influence) were significantly correlated with the strength of the association between a symptom’s change and the change in the severity of all other symptoms in the network from pre- to post-treatment (Δnode-Δnetwork association). Using similar analyses, we also examine the predictive validity of two simple non-causal node properties (mean symptom severity and infrequency of symptom endorsement). Results Of the three centrality measures, only expected influence successfully predicted how strongly changes in nodes/symptoms were associated with change in the remainder of the nodes/symptoms. Importantly, when excluding the amnesia node, a well-documented outlier in the phenomenology of PTSD, none of the tested centrality measures predicted symptom change. Conversely, both mean symptom severity and infrequency of symptom endorsement, two standard non-network-derived indices, were found to be more predictive than expected influence and remained significantly predictive also after excluding amnesia from the network analyses. Conclusions The centrality hypothesis in its current form is ill-defined, showing no consistent supporting evidence in the context of cross-sectional, between-subject networks

Delirium screening in an acute care setting with a machine learning classifier based on routinely collected nursing data: A model development study

Delirium screening in acute care settings is a resource intensive process with frequent deviations from screening protocols. A predictive model relying only on daily collected nursing data for delirium screening could expand the populations covered by such screening programs. Here, we present the results of the development and validation of a series of machine-learning based delirium prediction models. For this purpose, we used data of all patients 18 years or older which were hospitalized for more than a day between January 1, 2014, and December 31, 2018, at a single tertiary teaching hospital in Zurich, Switzerland. A total of 48,840 patients met inclusion criteria. 18,873 (38.6%) were excluded due to missing data. Mean age (SD) of the included 29,967 patients was 71.1 (12.2) years and 12,231 (40.8%) were women. Delirium was assessed with the Delirium Observation Scale (DOS) with a total score of 3 or greater indicating that a patient is at risk for delirium. Additional measures included structured data collected for nursing process planning and demographic characteristics. The performance of the machine learning models was assessed using the area under the receiver operating characteristic curve (AUC). The training set consisted of 21,147 patients (mean age 71.1 (12.1) years; 8,630 (40.8%) women|) including 233,024 observations with 16,167 (6.9%) positive DOS screens. The test set comprised 8,820 patients (median age 71.1 (12.4) years; 3,601 (40.8%) women) with 91,026 observations with 5,445 (6.0%) positive DOS screens. Overall, the gradient boosting machine model performed best with an AUC of 0.933 (95% CI, 0.929 - 0.936). In conclusion, machine learning models based only on structured nursing data can reliably predict patients at risk for delirium in an acute care setting. Prediction models, using existing data collection processes, could reduce the resources required for delirium screening procedures in clinical practice

Mapping the availability of translated versions of posttraumatic stress disorder screening questionnaires for adults: A scoping review

Background: The most used questionnaires for PTSD screening in adults were developed in English. Although many of these questionnaires were translated into other languages, the procedures used to translate them and to evaluate their reliability and validity have not been consistently documented. This comprehensive scoping review aimed to compile the currently available translated and evaluated questionnaires used for PTSD screening, and highlight important gaps in the literature. Objective: This review aimed to map the availability of translated and evaluated screening questionnaires for posttraumatic stress disorder (PTSD) for adults. Methods: All peer-reviewed studies in which a PTSD screening questionnaire for adults was translated, and which reported at least one result of a qualitative and /or quantitative evaluation procedure were included. The literature was searched using Embase, MEDLINE, and APA PsycInfo, citation searches and contributions from study team members. There were no restrictions regarding the target languages of the translations. Data on the translation procedure, the qualitative evaluation, the quantitative evaluation (dimensionality of the questionnaire, reliability, and performance), and open access were extracted. Results: A total of 866 studies were screened, of which 126 were included. Collectively, 128 translations of 12 different questionnaires were found. Out of these, 105 (83.3%) studies used a forward and backward translation procedure, 120 (95.2%) assessed the reliability of the translated questionnaire, 60 (47.6%) the dimensionality, 49 (38.9%) the performance, and 42 (33.3%) used qualitative evaluation procedures. Thirty-four questionnaires (27.0%) were either freely available or accessible on request. Conclusions: The analyses conducted and the description of the methods and results varied substantially, making a quality assessment impractical. Translations into languages spoken in middle- or low-income countries were underrepresented. In addition, only a small proportion of all translated questionnaires were available. Given the need for freely accessible translations, an online repository was developed

Structural neuroimaging of hippocampus and amygdala subregions in posttraumatic stress disorder: A scoping review

Numerous studies have explored the relationship between posttraumatic stress disorder (PTSD) and the hippo-campus and the amygdala because both regions are implicated in the disorder’s pathogenesis and pathophysiology. Nevertheless, those key limbic regions consist of functionally and cytoarchitecturally distinct substructures that may play different roles in the etiology of PTSD. Spurred by the availability of automatic segmentation software, structural neuroimaging studies of human hippocampal and amygdala subregions have proliferated in recent years. Here, we present a preregistered scoping review of the existing structural neuroimaging studies of the hippocampus and amygdala subregions in adults diagnosed with PTSD. A total of 3513 studies assessing subregion volumes were identified, 1689 of which were screened, and 21 studies were eligible for this review (total N = 2876 individuals). Most studies examined hippocampal subregions and reported decreased CA1, CA3, dentate gyrus, and subiculum volumes in PTSD. Fewer studies investigated amygdala subregions and reported altered lateral, basal, and central nuclei volumes in PTSD. This review further highlights the conceptual and methodological limitations of the current literature and identifies future directions to increase understanding of the distinct roles of hippocampal and amygdalar subregions in posttraumatic psychopathology

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements