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Thèse Année : 2023

Novel strategies for identifying and addressing mental health and learning disorders in school-age children

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.

Résumé

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.
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.
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Origine : Version validée par le jury (STAR)

Dates et versions

tel-04548892 , version 1 (16-04-2024)

Identifiants

  • HAL Id : tel-04548892 , version 1

Citer

Kseniia Konishcheva. Novel strategies for identifying and addressing mental health and learning disorders in school-age children. Psychology. Université Paris Cité, 2023. English. ⟨NNT : 2023UNIP7083⟩. ⟨tel-04548892⟩

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