A Longitudinal Study of Gaming Patterns During the First 11 Months of the COVID-19 Pandemic
Bibliographic record
Abstract
ABSTRACT Objectives: The objective of this study was to longitudinally study engagement in video gaming throughout the first 11 months of the COVID-19 pandemic using latent growth curve modeling. Methods: A total of 332 Canadian adults (M age=33.79, 60.8% men) who played video games were recruited from the crowdsourcing site Prolific. Participants responded to 4 waves of surveys (spaced 3 mo apart) from April 2020 to March 2021. The main outcome of interest was time spent gaming, measured in hours spent gaming in the past 30 days before each assessment wave. Results: Latent growth curve modelling showed that participants reported high initial levels of gaming, but progressively declined in gaming activity across the subsequent waves. Being male, living with others, experiencing a decrease in income because of COVID-19, endorsement of disordered gaming symptoms, game preference, and solitary gaming were significant predictors of increased gaming at the outset of the pandemic. However, only age was related to longer-term declines in gaming during the pandemic, such that older participants’ gaming decreased at a more accelerated rate. Conclusions: This study suggests that gaming declined over the course of the pandemic and was not a problematic behaviour on average among a community sample of Canadian adults during the COVID-19 pandemic. Objectifs: L’objectif de cette étude était d'étudier longitudinalement l’engagement dans les jeux vidéo au cours des onze premiers mois de la pandémie de COVID-19 en utilisant la modélisation de la courbe de croissance latente. Méthodes: Au total, 332 adultes canadiens (âge=33,79, 60,8% d’hommes) jouant à des jeux vidéo ont été recrutés sur le site de recrutement Prolific. Les participants ont répondu à quatre vagues d’enquêtes (espacées de 3 mois) d’avril 2020 à mars 2021. Le principal résultat d’intérêt était le temps passé à jouer, mesuré en heures passées à jouer au cours des 30 derniers jours précédant chaque vague d'évaluation. Résultats: La modélisation de la courbe de croissance latente a montré que les participants ont signalé des niveaux initiaux élevés de jeu, mais ont progressivement diminué leur activité de jeu au cours des vagues suivantes. Le fait d'être un homme, de vivre avec d’autres personnes, de subir une baisse de revenu en raison du COVID-19, d’endosser des symptômes de troubles du jeu, les préférences de jeux et de jouer en solitaire étaient des facteurs prédictifs significatifs d’une augmentation de l’activité de jeu au début de la pandémie. Cependant, seul l'âge était lié à une diminution à plus long terme du jeu pendant la pandémie, de sorte que le temps de jeu des participants plus âgés diminuait à un rythme plus rapide. Conclusions: Cette étude, prise parmi un échantillon communautaire d’adultes canadiens pendant la pandémie de COVID-19, suggère qu’en moyenne, le jeu a diminué au cours de la pandémie et n’a pas été un comportement problématique.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".