Digital Game Interventions for Youth Mental Health Services (Gaming My Way to Recovery): Protocol for a Scoping Review (Preprint)
Bibliographic record
Abstract
BACKGROUND Digital or video games are played by millions of adolescents and young adults around the world and are one of the technologies used by youths to access mental health services. Youths with mental health problems strongly endorse the use of technologies, including mobile and online platforms, to receive information, support their treatment journeys (eg, decision-making tools), and facilitate recovery. A growing body of literature explores the advantages of playing digital games for improving attention span and memory, managing emotions, promoting behavior change, and supporting treatment for mental illness (eg, anxiety, depression, or posttraumatic stress disorder). The research field has also focused on the negative impact of video games, describing potential harms related to aggression, addiction, and depression. To promote clarity on this matter, there is a great need for knowledge synthesis offering recommendations on how video games can be safely and effectively adopted and integrated into youth mental health services. OBJECTIVE The Gaming My Way to Recovery scoping review project assesses existing evidence on the use of digital game interventions within the context of mental health services for youths (aged 11-29 years) using the stepped care model as the conceptual framework. The research question is as follows: For which youth mental health conditions have digital games been used and what broad objectives (eg, prevention, treatment) have they addressed? METHODS Using the methodology proposed by Arksey and O’Malley, this scoping review will map the available evidence on the use of digital games for youths between 11 and 29 years old with mental health or substance use problems, or both. RESULTS The review will bring together evidence-based knowledge to assist mental health providers and policymakers in evaluating the potential benefits and risks of these interventions. Following funding of the project in September 2018, we completed the search in November 2018, and carried out data screening and stakeholder engagement activities during preparation of the protocol. We will conduct a knowledge synthesis based on specific disorders, treatment level and modality, type of service, population, settings, ethical practices, and user engagement and offer recommendations concerning the integration of video game technologies and programs, future research and practice, and knowledge dissemination. CONCLUSIONS Digital game interventions employ unique, experiential, and interactive features that potentially improve skills and facilitate learning among players. Digital games may also provide a new treatment platform for youths with mental health conditions. Assessing current knowledge on video game technology and interventions may potentially improve the range of interventions offered by youth mental health services while supporting prevention, intervention, and treatment. INTERNATIONAL REGISTERED REPORT PRR1-10.2196/13834
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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 | 0.050 | 0.058 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.121 | 0.017 |
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".