Affective teacher–child relationships have frequently been investigated in school settings, but less attention has been devoted to these relationships in after-school care. This study explored caregiver- (N = 90) and child-informed reports (N = 90) of the affective caregiver–child relationship (N = 180 dyads) in Dutch after-school care, exploring gender differences at caregiver and child level and the relationship with a gender match between children and caregivers. The caregivers and children reported relatively high levels of closeness and relatively low level of conflict and dependency/autonomy support, irrespective of gender. Multilevel regression analyses revealed that a gender match between child and caregiver was associated with teacher-reported closeness: levels were highest in female-girl dyads and lowest in male-boy dyads. Further, boys indicated the highest levels of autonomy in male-boy dyads, whereas girls indicated the lowest levels in female-girl dyads. Masculinity of staff was associated with more child-reported autonomy support, whereas femininity predicted caregiver-reported closeness in the relationship.
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The past two decades, a disproportionate growth of females entering the criminal justice system and forensic mental health services has been observed worldwide. However, there is a lack of knowledge on the background of women who are convicted for violent offenses. What is their criminal history, what are their motives for offending and in which way do they differ from men convicted for violent offenses? In this study, criminal histories and the offenses for which they were admitted to forensic care were analyzed of 218 women and 218 men who have been treated between 1984 and 2014 with a mandatory treatment order in one of four Dutch forensic psychiatric settings admitting both men and women. It is concluded that there are important differences in violent offending between male and female patients. Most importantly, female violence was more often directed towards their close environment, like their children, and driven by relational frustration. Furthermore, female patients received lower punishments compared to male patients and were more often considered to be diminished accountable for their offenses due to a mental illness.
MULTIFILE
BACKGROUND: Prediction models and prognostic scores have been increasingly popular in both clinical practice and clinical research settings, for example to aid in risk-based decision making or control for confounding. In many medical fields, a large number of prognostic scores are available, but practitioners may find it difficult to choose between them due to lack of external validation as well as lack of comparisons between them.METHODS: Borrowing methodology from network meta-analysis, we describe an approach to Multiple Score Comparison meta-analysis (MSC) which permits concurrent external validation and comparisons of prognostic scores using individual patient data (IPD) arising from a large-scale international collaboration. We describe the challenges in adapting network meta-analysis to the MSC setting, for instance the need to explicitly include correlations between the scores on a cohort level, and how to deal with many multi-score studies. We propose first using IPD to make cohort-level aggregate discrimination or calibration scores, comparing all to a common comparator. Then, standard network meta-analysis techniques can be applied, taking care to consider correlation structures in cohorts with multiple scores. Transitivity, consistency and heterogeneity are also examined.RESULTS: We provide a clinical application, comparing prognostic scores for 3-year mortality in patients with chronic obstructive pulmonary disease using data from a large-scale collaborative initiative. We focus on the discriminative properties of the prognostic scores. Our results show clear differences in performance, with ADO and eBODE showing higher discrimination with respect to mortality than other considered scores. The assumptions of transitivity and local and global consistency were not violated. Heterogeneity was small.CONCLUSIONS: We applied a network meta-analytic methodology to externally validate and concurrently compare the prognostic properties of clinical scores. Our large-scale external validation indicates that the scores with the best discriminative properties to predict 3 year mortality in patients with COPD are ADO and eBODE.
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