Jenny Eglinsky

Evidence-based practices (EBPs) can support the delivery of high-quality, evidence-informed care for children and adolescents, yet are rarely implemented in German Child and Youth Welfare Service (CYWS) facilities. Although attitudes toward EBPs have been associated with EBP use, implementation is also shaped by organizational and individual factors. However, little is known about these relationships among child welfare workers (CWWs) in German CYWS facilities. This study therefore examined attitudes toward EBPs and their associations with perceived implementation climate and professional quality of life among CWWs. Baseline data of 161 child welfare workers in 37 German CYWS facilities from multi-center cluster-randomized controlled trial BETTER CARE were analyzed. Associations between the implementation climate (Implementation Climate Scale (ICS)) and the Professional Quality of Life (ProQOL) on attitude toward EBPs (Evidence-Based Practice Attitude Scale (EBPAS)) were examined using LASSO-based predictor selection followed by multiple linear regression analyses with cluster-robust standard errors. The ICS subscales recognition ( β = 0.22, p = .002), rewards ( β = -0.21, p = .004), and selection for openness ( β = 0.26, p = .002), as well as the ProQOL subscales burnout ( β = -0.60, p < .001) and compassion satisfaction ( β = 0.32, p = .022) were significant predictors of the attitudes toward EBPs among CWWs. Among CWWs in German CYWS facilities, attitudes toward EBPs were associated with selected dimensions of implementation climate and professional quality of life. The findings suggest that attitudes toward EBPs should be considered within their organizational and individual context.

Background: Suicidality is a major public health concern worldwide. Evidence on the prevalence and risk factors of suicidality amongst unaccompanied young refugees (UYRs), a population already at risk for mental health disorders, is scarce. Methods: Given the complexity of individual risk factor constellations influencing suicidality, machine learning (ML) methods offer a statistical approach that can detect complex relations within the data. Four ML classifiers, (logistic regression (LR), random forest (RF), support vector machines (SVM), and extreme gradient boosting (XGB)) were trained on a dataset of n = 623 UYRs (M _age =16.77, SD = 1.34, range: 12–21), retrieved from the large-scale randomized controlled trial Better Care to predict suicidal ideation. Features used in the classifiers were age, gender, asylum status, having contact with the family, and whether parents are alive as well as clinically elevated post-traumatic stress symptoms (PTSS), depressive symptoms and past suicide attempts. The classifiers were then tested on the independent dataset of n = 94 UYRs (M _age =16.31, SD = 2.03, range: 5–21) retrieved from the screening tool porta project to examine their predictive performance. Results: The prevalence of past-week suicidal ideation in the combined sample of N = 717 was 18.13%. All classifiers yielded good predictive performance (accuracy 0.734–0.840, sensitivity 0.857, AUC 0.853–0.880). The most relevant features were past suicide attempts, PTSS and depressive symptoms as risk factors, and having a living mother as protective factor. Conclusions: Suicidal ideation is prevalent amongst UYRs, and using ML approaches, the classifiers were able to classify roughly 85% of the cases with suicidal ideation in the past week correctly as suicidal. Building on the findings of this study, screening for suicidality could be further improved by implementing ML classifiers in the assessment to highlight potential at risk cases early, and suitable interventions be developed.