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Thèse soutenue

Nouvelles stratégies d'identification et de gestion des troubles de la santé mentale et des troubles de l'apprentissage chez les enfants d'âge scolaire.

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Auteur / Autrice : Kseniia Konishcheva
Direction : Ariel Lindner, Arno Klein
Type : Thèse de doctorat
Discipline(s) : Neurosciences et troubles neuronaux
Date : Soutenance le 20/12/2023
Etablissement(s) : Université Paris Cité
Ecole(s) doctorale(s) : École doctorale Frontières de l'innovation en recherche et éducation (Paris ; 2006-....)
Partenaire(s) de recherche : Laboratoire : Evolution et ingénierie de systèmes dynamiques (Paris ; 2020-....)
Jury : Président / Présidente : Catherine Lord
Examinateurs / Examinatrices : Ariel Lindner, Arno Klein, Catherine Lord, Satrajit Ghosh, Yasser Khazaal, Nicole Landi
Rapporteurs / Rapporteuses : Satrajit Ghosh, Yasser Khazaal

Résumé

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The prevalence of mental health and learning disorders in school-age children is a growing concern. Yet, a significant delay exists between the onset of symptoms and referral for intervention, contributing to long-term challenges for affected children. The current mental health system is fragmented, with teachers possessing valuable insights into their students' well-being but limited knowledge of mental health, while clinicians often only encounter more severe cases. Inconsistent implementation of existing screening programs in schools, mainly due to resource constraints, suggests the need for more effective solutions. This thesis presents two novel approaches for improvement of mental health and learning outcomes of children and adolescents. The first approach uses data-driven methods, leveraging the Healthy Brain Network dataset which contains item-level responses from over 50 assessments, consensus diagnoses, and cognitive task scores from thousands of children. Using machine learning techniques, item subsets were identified to predict common mental health and learning disability diagnoses. The approach demonstrated promising performance, offering potential utility for both mental health and learning disability detection. Furthermore, our approach provides an easy-to-use starting point for researchers to apply our method to new datasets. The second approach is a framework aimed at improving the mental health and learning outcomes of children by addressing the challenges faced by teachers in heterogeneous classrooms. This framework enables teachers to create tailored teaching strategies based on identified needs of individual students, and when necessary, suggest referral to clinical care. The first step of the framework is an instrument designed to assess each student's well-being and learning profile. FACETS is a 60-item scale built through partnerships with teachers and clinicians. Teacher acceptance and psychometric properties of FACETS are investigated. Preliminary pilot study demonstrated overall acceptance of FACETS among teachers. In conclusion, this thesis presents a framework to bridge the gap in detection and support of mental health and learning disorders in school-age children. Future studies will further validate and refine our tools, offering more timely and effective interventions to improve the well-being and learning outcomes of children in diverse educational settings.