What Happens After Good Game?: Protocol of a Scoping Review on the Motivators and Mental Health Effects of Video Game Streaming
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.100 | 0.093 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.016 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.058 | 0.012 |
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".