Bastiaansen Jort A J, Veldhuizen Elien E, De Schepper Kees, Scheepers Floortje E
Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands.
Front Psychiatry. 2022 Apr 28;13:719598. doi: 10.3389/fpsyt.2022.719598. eCollection 2022.
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.
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.
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.
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.
相对较少的研究关注患有精神疾病诊断的儿童的兄弟姐妹的幸福感、经历和需求。然而，已开展的研究表明，这种情况对这些兄弟姐妹的影响是巨大的。研究已确诊儿童或亲属的叙述已被证明是一种成功的方法，可获得有助于改善护理的见解。在精神病学中，只有少数尝试利用机器学习方法来研究叙述。
在本研究中，通过基本无结构的访谈收集了13份关于神经发育障碍儿童兄弟姐妹经历的叙述。使用传统的定性、诠释现象学方法以及潜在狄利克雷分配（LDA）对访谈进行分析，LDA是一种无监督机器学习方法，可将文档中的单词聚类为主题。本研究的一个目的是评估兄弟姐妹的经历，以便找到改善对这些兄弟姐妹的护理和支持的线索。此外，比较了两种分析的结果，以评估机器学习在分析叙述中的作用。
访谈的定性分析得出了九个主要主题：面对冲突、兄弟姐妹的应对策略、休息和属于自己时间的需求、来自个人圈子的支持和关注的需求、对正常状态的渴望、对个人选择和发展可能性的影响、一起做事、建议和意见、矛盾心理和忠诚。使用无监督机器学习（LDA）形成了24个主题，这些主题大多与定性分析中发现的主题重叠。定性分析和LDA分析都检测到了各自分析特有的主题。
本研究发现，研究神经发育障碍儿童兄弟姐妹的叙述有助于更好地理解这些受试者的经历。兄弟姐妹应对对其兄弟姐妹的矛盾情感，这种情感冲突通常会导致行为调整。他们会制定几种应对策略来应对其兄弟姐妹的行为，如寻求支持或忽视。需要来自近亲，尤其是父母的全心支持、时间和关注。LDA分析似乎无助于从叙述中提取意义和背景，但有人提出，机器学习可以通过发现被忽视的主题并给出叙述中主题的初步概述，成为传统定性方法的一种有价值且快速的补充。
