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Enregistrement W2901414297

Violent Video Gaming, Parent and Child Risk Factors, and Aggression in School-Age Children

2018· article· en· W2901414297 sur OpenAlexfundaboutno aff
Erin Romanchych

Notice bibliographique

RevueScholarship at UWindsor (University of Windsor) · 2018
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueChild Development and Digital Technology
Établissements canadiensnon disponible
Organismes subventionnairesUniversity of Windsor
Mots-clésAggressionPsychologyDevelopmental psychologyHuman factors and ergonomicsPoison controlInjury preventionSuicide preventionMedicineMedical emergency
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The present study examined links between children’s violent video game exposure and aggression, and the influence of parent and child risk factors (i.e., children’s negative affect and hostile attribution bias, parental monitoring, and children’s gender). Participants were 122 Canadian parent-child dyads (99 unique parents) including children between 7 and 10 years of age (41 girls, 81 boys; 72 mothers, 26 fathers). Parents completed pencil-and-paper questionnaires assessing children’s violent video game exposure, aggressive behaviour, negative affect, and parental monitoring of children’s media use (i.e., parental involvement, limit setting, and communication). Children completed pencil-and-paper questionnaires assessing violent video game exposure and hostile attribution bias. Parents’ perceptions about children’s video gaming and links with aggression were also explored during semi-structured interviews with 15 of the parents (10 mothers, 5 fathers). The analyses revealed that higher levels of parent-reported children’s violent video game exposure predicted higher levels of aggression. In addition, higher levels of children’s negative affect predicted higher levels of children’s aggression. Children’s negative affect was found to mediate the relation between children’s violent video game exposure (parent report) and aggression, such that higher levels of children’s violent video game exposure indirectly related to higher levels of children’s aggression, through higher levels of negative affect. Children’s hostile attribution bias was not predictive of children’s aggression, nor did it mediate the link between children’s violent video game exposure and aggression. In terms of parental monitoring, higher levels of children’s violent video game exposure were related to higher levels of parental involvement and communication. None of the parental monitoring variables (i.e., parental involvement, limit setting, and communication) were related to children’s aggression. The relation between children’s violent video game exposure and aggression did not vary based on levels of parental monitoring or children’s gender. Results from the thematic analysis of the interview data supported these findings. Parents believed that exposure to children’s violent video games would increase their risk of engaging in real world violence and imitating aggressive or violent behaviours from the video games. Parents also reported that children experienced negative reactions, such as aggression, to playing video games -- including violent video games. Parents thought that children’s reactions to playing violent video games varied based on children’s temperament, and that children might be at greater risk of experiencing negative reactions if they had certain traits (e.g., overly emotional, angry). In terms of parental monitoring, parents were more likely to monitor children’s gaming if parents, themselves, were interested in gaming or if children were playing games with violent content. Parents were more likely to discuss gaming with their children when children played video games with violent content. Similarly, parents tended to set limits on the content children were exposed to (i.e., violent games); however, most children were exposed to violence in video games. Overall, these findings identify parent and child factors (i.e., children’s negative affect, parental involvement and communication) that may mitigate or exacerbate the effects of playing violent video games, which can be useful for education on media use, intervention programs, and directions for future research.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,008
Score d'incertitude au seuil0,876

Scores Codex et Gemma par catégorie

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

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 Opus0,016
Tête enseignante GPT0,244
Écart entre enseignants0,228 · 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 tête enseignante, 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

Citations1
Publié2018
Routes d'admission2
Résumé présentoui

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