The importance of informal support and a listening ear: A narrative analysis of emotional conflict and support needs of siblings of children with a psychiatric disorder.

This study investigates the self-reported impact of children's psychiatric disorders on their siblings and assesses what forms of support such children most value. We used a qualitative research design with open interviews to stimulate children between 8 and 15 years old to talk about their experiences living with a brother or sister with a psychiatric disorder. Their stories were analysed within a hermeneutic phenomenological framework in order to identify narrative themes and interpret the meaning of shared experiences. From our analysis, nine shared narrative themes emerge. Overall, siblings report feeling conflicted about adapting their lives to their brother's or sister's disorder and signal a need for personalized attention from parents. They also indicate that being involved in the care for their brother or sister helps them to better understand their behaviour. Finally, siblings reveal that, in their experience, formal, protocolized forms of support foreground family problems and stress. Thus, we recommend to involve children in the care process; to acknowledge their personal needs and conflicts; and to be mindful of the style of support: help offered in an informal or playful way, instead of formal and protocolized, could be a more effective way of meeting siblings' needs.

Experiences of Siblings of Children With Neurodevelopmental Disorders: Comparing Qualitative Analysis and Machine Learning to Study Narratives.

Introduction: Relatively few studies have focused on the wellbeing, experiences and needs of the siblings of children with a psychiatric diagnosis. However, the studies that have been conducted suggest that the impact of such circumstances on these siblings is significant. Studying narratives of diagnosed children or relatives has proven to be a successful approach to gain insights that could help improve care. Only a few attempts have been made to study narratives in psychiatry utilizing a machine learning approach. Method: In this current study, 13 narratives of the experiences of siblings of children with a neurodevelopmental disorders were collected through largely unstructured interviews. The interviews were analyzed using the traditional qualitative, hermeneutic phenomenology method as well as latent Dirichlet allocation (LDA), an unsupervised machine learning method clustering words from documents into topics. One aim of this study was to evaluate the experiences of the siblings in order to find leads to improve care and support for these siblings. Furthermore, the outcomes of both analyses were compared to evaluate the role of machine learning in analyzing narratives. Results: Qualitative analysis of the interviews led to the formulation of nine main themes: confrontation with conflicts, coping strategies siblings, need for rest and time for myself, need for support and attention from personal circle, wish for normality, influence on personal choices and possibilities for development, doing things together, recommendations and advices, ambivalence and loyalty. Using unsupervised machine learning (LDA) 24 topics were formed that mostly overlapped with the qualitative themes found. Both the qualitative analysis and the LDA analysis detected themes that were unique to the respective analysis. Conclusion: The present study found that studying narratives of siblings of children with a neurodevelopmental disorder contributes to a better understanding of the subjects' experiences. Siblings cope with ambivalent feelings toward their brother or sister and this emotional conflict often leads to adapted behavior. Several coping strategies are developed to deal with the behavior of their brother or sister like seeking support or ignoring. Devoted support, time and attention from close relatives, especially parents, is needed. The LDA analysis didn't appear useful to distract meaning and context from the narratives, but it was proposed that machine learning could be a valuable and quick addition to the traditional qualitative methods by finding overlooked topics and giving a rudimental overview of topics in narratives.