Understanding the game experiences and mental health of youth: protocol for the Game-in-Action Quebec cohort study
Bibliographic record
Abstract
INTRODUCTION: Video games have been linked to a range of positive and negative effects on the mental health of adolescents and young adults. However, to better understand how games affect the mental health of young people, their use and experiences must be situated in the sociocultural and personal life contexts of individuals. Drawing from a cultural-ecosocial approach, this study combines cross-sectional and digital phenotyping measures to examine the effects of video games on the mental health of youth. METHODS AND ANALYSIS: Participants will be young people aged 16-25 years from the community and living in the province of Quebec, Canada. An initial sample of 1000 youth will complete a cross-sectional survey online, including measures of socio-demographic context, gaming practices and experiences, streaming practices and experiences, as well as personality and well-being. Qualitative questions will explore personal views on games and mental health. A subsample of 100 participants will be selected for digital phenotyping, including daily surveys of well-being, gaming, streaming and social experiences, combined with passive mobile sensing (eg, geolocation). Analyses will include regression and mixed models for quantitative data, reflexive thematic analysis for qualitative data, and an integration of quantitative and qualitative results using participatory methods. ETHICS AND DISSEMINATION: The study received ethical approval from the Institutional Review Board of McGill University (24-02-015). The dissemination of results will be conducted in partnership with a multi-stakeholder advisory committee, including youth who play video games, and will involve peer-reviewed publications, presentations to policymakers in Quebec, and workshops for clinicians and researchers.
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 | 0.020 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.061 | 0.009 |
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".