School Closures on Bullying Experiences of Treatment-Seeking Children and Youth: The Influence of the COVID-19 Pandemic Within Ontario, Canada
Notice bibliographique
Résumé
Amongst school-aged children and youth, bullying is a significant problem warranting further investigation. The current study sought to investigate the influence of the COVID-19 pandemic waves and school closures on the bullying experiences of 22,012 children aged 4-18-years-old who were referred and assessed at mental health agencies in Ontario, Canada. Individual, familial, and mental health variables related to bullying experiences were also investigated. Data were collected from January 2017 to February 2022. The pre-pandemic period of study included January to June 2017, September 2018/2019 to June 2019/2020. The pandemic period was divided into categories of remote learning (17 March 2020 to 30 June 2020, 8 January 2021 to 16 February 2021, 12 April 2021 to 30 June 2021) and in-person learning (remaining pandemic dates). The summer holidays pre-pandemic were in July-August 2017, 2018, 2019 and during the pandemic they were in July-August 2020 and 2021. Logistic regressions were conducted to analyze data. Findings related to COVID-19 showed bullying rates to be lower during the pandemic when compared to pre-pandemic levels (bullied others during pandemic in school: OR = 0.44, CI = 0.34-0.57; victim of bullying during pandemic in school: OR = 0.41, CI = 0.33-0.5). Furthermore, bullying rates were lower during the pandemic periods when schools were closed for in-person learning (bullied others during pandemic remote: OR = 0.62, CI = 0.45-0.85; victim of bullying during pandemic remote: OR = 0.24, CI = 0.17-0.34). Children who lived in lower income areas, experienced home life challenges, exhibited mental health difficulties, or had behavioural concerns were more likely to be involved in bullying experiences. Finally, classroom type and school program impacted the child's likelihood of bullying others or being bullied. These findings further our understanding of the impact of school closures on children's mental health and behaviour during the pandemic. Public health and policy implications such as bullying prevention, supervision, and conflict management are discussed.
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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 | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,005 | 0,002 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 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 ».