A longitudinal investigation of school absenteeism and mental health challenges among Canadian children and youth in the COVID-19 context
Notice bibliographique
Résumé
School absenteeism across the globe has risen dramatically since the COVID-19 pandemic. Literature indicates that children and youth of all ages are struggling to attend school regularly, leading to problematic outcomes both concurrently and across time. As well, research demonstrates that children and youth who experience mental health challenges are at greater risk of increased school absenteeism rates. The present study investigated the school attendance patterns of Canadian children and youth and the longitudinal and bidirectional links with mental health challenges within the COVID-19 pandemic context. The study sample consisted of 72 children and youth, using parent reports. Parents were asked to complete an online questionnaire which included questions about the demographic characteristics of themselves and their child, their child's school attendance patterns, and their child's mental health challenges. Preliminary descriptive statistics were run in relation to school absenteeism. Two separate path analyses were conducted to determine the longitudinal links between school absenteeism and mental health (split into externalizing and internalizing behaviours) across two timepoints (Time 1 [T1]: Fall 2022, Time 2 [T2]: Spring 2023). These analyses indicated concurrent links between mental health difficulties and school absenteeism. Importantly, path analyses also showed that absenteeism at T1 predicted poorer mental health at T2, indicating that school absenteeism may be one of the driving factors in the causal relationship. A bidirectional effect was found between externalizing behaviours at T1 and absenteeism rates at T2. The reasons for school absenteeism were examined across each time point and for both the externalizing and internalizing groups separately. The present study highlights the complex interplay between mental health and school absenteeism in the context of the COVID-19 pandemic. It provides avenues for effective intervention to better support children and youth struggling with mental health and school absenteeism.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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 tête enseignante, 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 ».