Time Spent Gaming, Device Type, Addiction Scores, and Well-being of Adolescent English Gamers in the 2021 OxWell Survey: Latent Profile Analysis
Notice bibliographique
Résumé
BACKGROUND: The shift in the last decades to screen-based and increasingly web-based gaming activity has raised concerns about its impact on the development of children and adolescents. Despite decades of research into gaming and related psychosocial effects, the question remains how best to identify what degree or context of gaming may be a cause for concern. OBJECTIVE: This study aimed to classify adolescents into gamer profiles based on both gaming behaviors and well-being. Once we distinguished the different gamer profiles, we aimed to explore whether membership to a specific profile could be predicted based on a range of personal characteristics and experiences that could then help identify those at risk. METHODS: We explored gaming and well-being in an adolescent school population (aged 12-18 years) in England as part of the 2021 OxWell student survey. Self-report measures of time spent playing games on computers or consoles, time spent playing games on mobile phones, the Game Addiction Scale, and the Warwick-Edinburgh Mental Well-being Scale were used to classify adolescent heavy gamers (playing games for at least 3.5 hours a day) using latent profile analysis. We used multinomial logistic regression analysis to predict the profile membership based on a range of personal characteristics and experiences. RESULTS: In total, 12,725 participants answered the OxWell gaming questions. Almost one-third (3970/12,725, 31.2%) indicated that they play games for at least 3.5 hours a day. The correlation between time spent playing video games overall and well-being was not significant (P=.41). The latent profile analysis distinguished 6 profiles of adolescent heavy gamers: adaptive computer gamers (1747/3970, 44%); casual computer gamers (873/3970, 22%); casual phone gamers (595/3970, 15%); unknown device gamers (476/3970, 12%); maladaptive computer gamers (238/3970, 6%); and maladaptive phone gamers (79/3970, 2%). In comparison with adaptive computer gamers, maladaptive phone gamers were mostly female (odds ratio [OR] 0.08, 95% CI 0.03-0.21) and were more likely to have experienced abuse or neglect (OR 3.18, 95% CI 1.34-7.55). Maladaptive computer gamers, who reported gaming both on their mobile phones and on the computer, were mostly male and more likely to report anxiety (OR 2.25, 95% CI 1.23-4.12), aggressive behavior (OR 2.83, 95% CI 1.65-4.88), and web-based gambling (OR 2.18, 95% CI 1.24-3.81). CONCLUSIONS: A substantial number of adolescents are spending ≥3.5 hours gaming each day, with almost 1 in 10 (317/3970, 8%) reporting co-occurring gaming and well-being issues. Long hours gaming using mobile phones, particularly common in female gamers, may signal poorer functioning and indicate a need for additional support. Although increased time gaming might be changing how adolescents spend their free time and might thus have public health implications, it does not seem to relate to co-occurring well-being issues or mental ill-health for the majority of adolescent gamers.
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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| 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 ».