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Enregistrement W6921870992 · doi:10.7939/r3-gynm-5x85

Children and Youth Mental Health: The Role of a Collaborative, School-Based Wraparound Support Intervention in Fostering Mental Health

2023· dissertation· en· W6921870992 sur OpenAlexaboutno aff

Notice bibliographique

RevueUniversity of Alberta Library · 2023
Typedissertation
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueDiverse Scientific and Economic Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMental healthPsychological interventionIntervention (counseling)Focus groupLeverage (statistics)Mental illness

Résumé

récupéré en direct d'OpenAlex

There is an increased recognition that early mental health interventions are needed in response to a growing mental health crisis among children and youth. Schools are promising sites for early intervention because they have existing infrastructure for accessing and engaging with students. Specifically, collaborative initiatives involving community partnerships allow schools to leverage shared resources to deliver mental health support. However, more research is needed to guide the development and implementation of early interventions so that they effectively address the mental health needs of children and youth. Therefore, the present study explored the role of collaborative, school-based mental health services in fostering children and youth’s mental health, through All in for Youth, a wraparound model of support in Edmonton, Canada. Three research questions were addressed: (1) What mental health concerns do children and youth experience? (2) What are the factors that impact the use of collaborative school-based mental health services? (3) Does a collaborative school-based approach to mental health services lead to perceived mental health impacts among children and youth (i.e., emotionally, psychologically, and/or behaviourally)? A multiple methods secondary analysis was conducted to address this research inquiry. Interview and focus group data generated with students (n = 51 students; grades 2 – 9) and parents/caregivers (n = 18) across seven AIFY elementary and junior high schools were analyzed to understand participants’ experiences with collaborative, school-based mental health services. Additionally, school cohort data (n = 7 schools; n = 2,073 students) were analyzed with information on students’ socio-demographic characteristics and use of services across schools. The quantitative findings indicated that overall, n = 885 students (42.7%) accessed any type of mental health service across the seven schools, with close to equivalent service use by gender (50.2% male, 49.5% female, 0.3% genderqueer) and grade level (kindergarten – grade 9; M = 10%, SD = 1.9%, range = 6.3–13%). There was also high service use across diverse student statuses (Indigenous , 24.5%; Refugee, 9.5%; English language learner, 30.1%; specialized learning needs, 18.7%). Participants accessed mental health services in primarily individual or combined individual and group settings (72.9%) and as an informal, short-term client (75.1%). Furthermore, many service users went on to use two or more mental health services (42.2%). The interview and focus group findings revealed high mental health needs among students, which were further exacerbated by the COVID-19 pandemic. In response to these needs, a supportive school culture, adequate school communication, and a stable and well-resourced mental health workforce promoted access to collaborative, school-based mental health services. Finally, mental health services were described to support children and youth through the experience of having a supportive relationship with a safe and caring adult, developing an improved capacity to cope with school and life, and improved overall family functioning. The findings underscore the importance of developing school-based mental health services that recruit school-community partnerships on the delivery of services and take an ecological, wraparound approach to addressing students’ multi-faceted mental health needs. Study implications and future directions for research are discussed.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,078
Score d'incertitude au seuil0,154

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0030,001
Communication savante0,0020,001
Science ouverte0,0010,003
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,018
Tête enseignante GPT0,203
Écart entre enseignants0,186 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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