Barriers to Learning: The Case for Integrated Mental Health Services in Schools
Notice bibliographique
Résumé
D. S. Lean & V. A. Colucci (2010). Barriers to learning: The case for integrated mental health services in schools. Plymouth, UK: Rowman & Littlefield Education. 120 pp., ISBN 978-1-60709-638-2 (paperback) Barriers to Learning: The Case for Integrated Mental Health Services in Schools, which brings timely attention to the issue of increasing pediatric mental health problems, presents a need for integrated mental health resources in the school setting. The title of this book accurately summarizes the focus of the work: the issues that impede student learning and the solutions necessary to improve student achievement. Although Michael Fullan, in his preface, suggests that this book is a must read for education reformers, I would encourage a read by individuals interested in pediatric health and education. As the authors point out, silos exist in the present education system and creating understanding of the barriers among concerned stakeholders and their agencies can be achieved by reading this book. The book, well organized into seven chapters, begins with a prologue where the authors capture the attention of the readers by presenting a vivid and realistic depiction of a typical school day in a non-integrated school system. The detailed account of each character, their behaviors and the particular barriers they faced, immediately drew my interest into the book. In the first chapter, the authors outline the increase in mental health issues and distinguish the two classifications of support services presently available: school-based and school-linked. Further, they define barriers to learning as a temporary or permanent factor, condition, or situation that obstructs or impedes academic progress, resulting in mild to severe effects. A detailed conceptualization depicts two types of barriers to learning, biological-psychological and environmental-circumstantial, as well as two types of outcomes, negative and positive. This conceptualization (represented diagrammatically) depicts how a reduction and prevention of barriers can ameliorate student symptoms and increase student achievement. Additionally, these authors argue that a broad base of professionals, situated uniquely in a school, is necessary to make accurate diagnoses and to recommend appropriate early interventions. The authors also recommend a universal preventative approach to target interventions, based on locally predetermined student needs. In the second and third chapters, the authors identify biological-psychological and environmental-circumstantial barriers to learning. The authors interestingly present the disorders and their descriptions in the order in which they are most likely to be referred, rather than by prevalence. They suggest that biological-psychological barriers to learning, which are the most common, are often the only barriers considered when students have difficulties because school systems are structured to primarily deal with these particular barriers. The author presents useful examples of environmental-circumstantial and biological-psychological symptoms, making the argument that more attention needs to be given to the former symptoms to protect against misdiagnosis and ineffective treatment. Along with describing inadequate interventions, the fourth chapter lists examples of negative outcomes (bullying, school refusal, addictions, early school leaving, suicide, and youth violence) that occur when students receive inadequate interventions to address barriers to learning. In addition to these outcomes, the authors propose that students who are not facing barriers to learning may experience a negative multi-ripple process whereby their academic progress is also compromised because of inadequately addressing other students' barriers. The fifth chapter of the book includes a brief literature review of present and proposed reforms in the education system, which more recently are driven by student achievement (through school leadership and pedagogy) and the mental health system. …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,018 | 0,047 |
| Communication savante | 0,021 | 0,020 |
| Science ouverte | 0,005 | 0,022 |
| Intégrité de la recherche | 0,015 | 0,021 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».