Older people with confused behavior, have behavioral problems due to dementia, mental problems or social problems. For the Healthcare Assistant (HA) in district nursing, it is a daily challenge to care for older people with confused behavior. Aim of this research is to achieve an insight in the strategies the HA uses to deal with the daily care for older people with confused behavior. It is also the aim to have insight in factors which contribute to the daily care in a positive or negative way. Method: a qualitative explorative research. 17 HA’s in district nursing participated in semi-structured interviews. All respondents had experience with caring for older people with confused behavior. The most important influencing factors are the experienced relationship between HA and the client and the experienced support by the team. Particularly behavioral problems due to mental problems can impede a relationship with the client. Further research is recommended to study the level of knowledge and competences of all levels of employees in district nursing.
MULTIFILE
AI tools increasingly shape how we discover, make and experience music. While these tools can have the potential to empower creativity, they may fundamentally redefine relationships between stakeholders, to the benefit of some and the detriment of others. In this position paper, we argue that these tools will fundamentally reshape our music culture, with profound effects (for better and for worse) on creators, consumers and the commercial enterprises that often connect them. By paying careful attention to emerging Music AI technologies and developments in other creative domains and understanding the implications, people working in this space could decrease the possible negative impacts on the practice, consumption and meaning of music. Given that many of these technologies are already available, there is some urgency in conducting analyses of these technologies now. It is important that people developing and working with these tools address these issues now to help guide their evolution to be equitable and empower creativity. We identify some potential risks and opportunities associated with existing and forthcoming AI tools for music, though more work is needed to identify concrete actions which leverage the opportunities while mitigating risks.
MULTIFILE
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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