Time to Think “Meta”: A Critical Viewpoint on the Risks and Benefits of Virtual Worlds for Mental Health
Bibliographic record
Abstract
The metaverse is gaining traction in the general population and has become a priority of the technological industry. Defined as persistent virtual worlds that exist in virtual or augmented reality, the metaverse proposes to afford a range of activities of daily life, from socializing and relaxing to gaming, shopping, and working. Because of its scope, its projected popularity, and its immersivity, the metaverse may pose unique opportunities and risks for mental health. In this viewpoint article, we integrate existing evidence on the mental health impacts of video games, social media, and virtual reality to anticipate how the metaverse could influence mental health. We outline 2 categories of mechanisms related to mental health: experiences or behaviors afforded by the metaverse and experiences or behaviors displaced by it. The metaverse may benefit mental health by affording control (over an avatar and its virtual environment), cognitive activation, physical activity, social connections, and a sense of autonomy and competence. However, repetitive rewarding experiences may lead to addiction-like behaviors, and high engagement in virtual worlds may facilitate and perpetuate the avoidance of challenges in the offline environment. Further, time spent in virtual worlds may displace (reduce) other determinants of mental health, such as sleep rhythms and offline social capital. Importantly, individuals will differ in their uses of and psychological responses to the metaverse, resulting in heterogeneous impacts on their mental health. Their technological motivations, developmental stage, sociodemographic context, and prior mental health problems are some of the factors that may modify and frame the positive and negative effects of the metaverse on their mental health. In conclusion, as the metaverse is being scaffolded by the industry and by its users, there is a window of opportunity for researchers, clinicians, and people with lived experience to coproduce knowledge on its possible impacts on mental health and illness, with the hope of influencing policy-making, technological development, and counseling of patients.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.007 | 0.083 |
| Scholarly communication | 0.013 | 0.024 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 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".