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Children's participation in legal proceedings affecting them personally has been gaining importance. So far, a primary research concern has been how children experience their participation in court proceedings. However, little is known about the child's voice itself: Are children able to clearly express their wishes, and if so, what do they say in child protection cases? In this study, we extracted information about children's statements from court file data of 220 child protection cases in Germany. We found 182 children were asked about their wishes. The majority of the statements found came either from reports of the guardians ad litem or from judicial records of the child hearings. Using content analysis, three main aspects of the statements were extracted: wishes concerning main place of residence, wishes about whom to have or not contact with, and children granting decision-making authority to someone else. Children's main focus was on their parents, but others (e.g., relatives and foster care providers) were also mentioned. Intercoder agreement was substantial. Making sure that child hearings are as informative as possible is in the child's best interest. Therefore, the categories developed herein might help professionals to ask questions more precisely relevant to the child.
Hintergrund
Adipositas ist im Kindes- und Jugendalter stark verbreitet. Medizinische Rehabilitationsmaßnahmen mit ihrem umfassenden Behandlungsangebot stellen eine wesentliche Säule der Versorgung dar. Da Adipositas mit vielfältigen psychosozialen Belastungen verbunden ist, stellt sich die Frage, ob psychotherapeutische Angebote noch stärker berücksichtigt werden sollten.
Fragestellung
Untersucht wurde, wie verbreitet psychische Auffälligkeiten bei Kindern und Jugendlichen mit Adipositas sind und in welchem Zusammenhang sie zum Gewichtsverlauf stehen.
Material und Methoden
Die Stichprobe bestand aus 220 Kindern und Jugendlichen mit Adipositas (8 bis 16 Jahre, M = 13,11 Jahre; SD ± 1,88 Jahre; 54,5 % weiblich), die an einer stationären Rehabilitationsmaßnahme teilnahmen. Emotionale- und Verhaltensauffälligkeiten (Strengths and Difficulties Questionnaire, SDQ) wurden zu Rehabilitationsbeginn sowie 6 und 12 Monate nach Rehabilitationsende im Elternbericht erfasst. Zudem wurden Daten zur Bestimmung des Gewichtstatus durch das medizinische Personal der Kliniken bzw. in der Katamnese von Hausärzten erhoben.
Ergebnisse
Fast die Hälfte der Kinder und Jugendlichen (48,6 %) wies auffällige Werte auf; v. a. Mädchen waren signifikant häufiger betroffen. Die deskriptive Betrachtung nach Rehabilitationsende zeigte einen vergleichbar hohen Anteil. Zudem wirkte sich das Vorliegen psychosozialer Auffälligkeiten signifikant negativ auf den Gewichtsverlauf aus.
Schlussfolgerung
Psychische Probleme sollten im Rahmen der Adipositastherapie stärker berücksichtigt werden. Zum einen sollten evtl. belastete Kinder durch Screenings identifiziert werden, zum anderen psychotherapeutische Maßnahmen zur Reduktion psychosozialer Belastungen integraler Bestandteil der Behandlung sein.
Background:
Childhood and adolescence are critical stages of life for mental health and well-being. Schools are a key setting for mental health promotion and illness prevention. One in five children and adolescents have a mental disorder, about half of mental disorders beginning before the age of 14. Beneficial and explainable artificial intelligence can replace current paper- based and online approaches to school mental health surveys. This can enhance data acquisition, interoperability, data driven analysis, trust and compliance. This paper presents a model for using chatbots for non-obtrusive data collection and supervised machine learning models for data analysis; and discusses ethical considerations pertaining to the use of these models.
Methods:
For data acquisition, the proposed model uses chatbots which interact with students. The conversation log acts as the source of raw data for the machine learning. Pre-processing of the data is automated by filtering for keywords and phrases.
Existing survey results, obtained through current paper-based data collection methods, are evaluated by domain experts (health professionals). These can be used to create a test dataset to validate the machine learning models. Supervised learning
can then be deployed to classify specific behaviour and mental health patterns.
Results:
We present a model that can be used to improve upon current paper-based data collection and manual data analysis methods. An open-source GitHub repository contains necessary tools and components of this model. Privacy is respected through
rigorous observance of confidentiality and data protection requirements. Critical reflection on these ethics and law aspects is included in the project.
Conclusions:
This model strengthens mental health surveillance in schools. The same tools and components could be applied to other public health data. Future extensions of this model could also incorporate unsupervised learning to find clusters and patterns
of unknown effects.