What Happens After Good Game?: Protocol of a Scoping Review on the Motivators and Mental Health Effects of Video Game Streaming
Notice bibliographique
Résumé
Video game streaming, a form of real-time social media that integrates traditional broadcasting and online gaming, continues to grow in popularity among young people in Canada and internationally, particularly through platforms like Twitch and Kick. Stream use involves four roles: either streaming oneself to an audience while playing video games, commentating while watching another gamer play, moderating the stream chat to ensure conduct guidelines are being upheld, or viewing a streamed video game. Despite widespread assumptions that video games are harmful and limited knowledge surrounding the effects of video game streams on young people, current research findings suggest potential mental health benefits of video game stream exposure. The motivation to engage with streams also remains unclear and may include aspects such as entertainment, skill development, social or community connection, and stream-related career aspirations. This scoping review aims to identify and synthesize the existing and emerging knowledge surrounding the mental health outcomes of stream users and motivators for engaging with streams. The scoping review will be conducted in accordance with the PRISMA for Scoping Reviews (PRISMA-ScR) checklist, as well as Arksey and O’Malley’s framework for scoping reviews. Six databases will be searched in March 2025: Medline (OVID), EMBASE (OVID), CINAHL (EBSCO), Scopus (Elsevier), PsychINFO (OVID), and Medline (Web of Science). The search strategy was developed in consultation with the McGill Library team. Studies from 2011 (e.g., the year the Twitch streaming platform was established) onwards that explore the effects of streaming on mental health and the motivators to engage with streams will be included. The screening process will take place in two phases, whereby a title-abstract screening of eligibility criteria will be conducted in March 2025, followed by full-text screenings by four independent reviewers. The Rayyan platform will be used to manage the review process. In tandem to traditional screening procedures, ASReview will assist with the screening processes, with the review team training the AI software and implementing quality checks. A narrative synthesis approach will integrate findings from studies and provide a qualitative understanding of the motivation for engaging with streams and the effects stream exposure has on mental health. Numerical and content analyses will be conducted to synthesize the data and present the most salient findings. Motivators for engaging with streams will be explored using the model of player motivations in online games as a theoretical framework and a realist methodology will be used as a framework to explore how social and psychological needs, such as digital empowerment, are fulfilled through streaming. Both frameworks have previously been successfully used to understand the motivations and effects of digital interventions and gaming among youth. This protocol is registered with Open Science Framework. The findings of this review will inform the research questions developed for a future research project utilizing a cross-sectional questionnaire to explore the impacts of video game streaming on mental health among youth residing in Quebec aged 16 to 25 years old.
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 | 0,100 | 0,093 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,005 |
| Méta-épidémiologie (sens large) | 0,014 | 0,016 |
| Bibliométrie | 0,014 | 0,011 |
| Études des sciences et des technologies | 0,005 | 0,005 |
| Communication savante | 0,007 | 0,008 |
| Science ouverte | 0,005 | 0,006 |
| Intégrité de la recherche | 0,010 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,058 | 0,012 |
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 ».