The Association Between Gaming Practices and Scholastic Performance Among Medical Students in India: Case-Control Study
Notice bibliographique
Résumé
BACKGROUND: Gaming is a billion-dollar industry that is expanding at a compound annual growth rate of 9% to 14.3%, with the biggest market in Southeast Asian countries. The availability of low-cost smartphones and the ease at which the internet can be accessed have made gaming popular among youth, who enjoy it as a leisure activity. According to the World Health Organization, excessive indulgence in gaming can lead to gaming disorder. Medical students indulging in excessive gaming can succumb to gaming disorder, which can affect their scholastic performance. OBJECTIVE: This study aimed to assess the association between gaming practices and scholastic performance among medical students. METHODS: This study used a case-control design, where 448 medical undergraduate students (first year to prefinal) were preliminarily surveyed using universal sampling on their gaming practices in the last 6 months. Out of this sample, the 91 participants who admitted to gaming in the past 6 months were recruited as cases, while participants who never engaged in gaming in the last 6 months were recruited as controls. Both the cases and controls were matched for age and gender in a 1:1 ratio. The internal assessment scores (based on 2 midterms completed in the last 6 months) of cases and controls were compared. The Snedecor F test was used to determine the association between the number of hours spent gaming and internal assessment scores (%), while the Student t test was used to determine significant differences between the internal assessment scores of cases and controls. Odds ratios were calculated to identify the risk of poor scholastic performance among cases compared to the controls. The prevalence of gaming disorder among cases was assessed using the Gaming Addiction Scale (GAS). RESULTS: The frequency of gaming (in hours) was not associated with mean internal assessment scores (P=.13). Male cases reported significantly lower internal assessment scores compared to male controls (P=.005 vs P=.01), whereas no significant differences were observed between the internal assessment scores of female cases and controls (P=.89 vs P=.59). A negative correlation was observed between GAS scores and internal assessment scores (r=-0.02). The prevalence of gaming disorder using the GAS was observed to be 6.3% (28/448) in the study population and 31% (28/91) among cases. The risk of low scores (<50%) among gamers was observed to be 1.9 (95% CI 1.04-3.44, P=.03) times higher in the first midterm and 1.80 (95% CI 0.97-3.36, P=.06) times higher in the second midterm compared to nongamers. CONCLUSIONS: The findings suggest that excessive gaming adversely affects the scholastic performance of male participants more than female participants. Awareness about gaming disorder needs to be created among students, parents, and teachers. Treatment services should be made available to medical students with gaming disorders.
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 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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».