Gaming With Stigma: Analysis of Messages About Mental Illnesses in Video Games
Bibliographic record
Abstract
BACKGROUND: Video game playing is a daily activity for many youths that replaces other media forms (eg, television); it serves as an important source of knowledge and can potentially impact their attitudes and behaviors. Researchers are, thus, concerned with the impact of video gaming on youth (eg, for promoting prosocial or antisocial behavior). Studies have also begun to explore players' experience of gameplay and video game messages about violence, sexism, and racism; however, little is known about the impact of commercial video games in the sharing and shaping of knowledge, and messages about mental illness. OBJECTIVE: The aim of this review was to identify how mental illness, especially psychosis, is portrayed in commercial video games. METHODS: We performed keyword searches on games made available between January 2016 and June 2017 on Steam (a popular personal computer gaming platform). A total of 789 games were identified and reviewed to assess whether their game content was related to mental illness. At the end of the screening phase, a total of 100 games were retained. RESULTS: We used a game elements framework (characters, game environment/atmosphere, goals, etc) to describe and unpack messages about mental health and illness in video games. The majority of the games we reviewed (97%, 97/100) portrayed mental illness in negative, misleading, and problematic ways (associating it with violence, fear, insanity, hopelessness, etc). Furthermore, some games portrayed mental illness as manifestations or consequences of supernatural phenomena or paranormal experiences. Mental illness was associated with mystery, the unpredictable, and as an obscure illness; its treatment was also associated with uncertainties, as game characters with mental illness had to undergo experimental treatment to get better. Unfortunately, little or no hope for recovery was present in the identified video games, where mental illness was often presented as an ongoing struggle and an endless battle with the mind and oneself. CONCLUSIONS: The game elements of the identified commercial video games included mental illness, about which many perpetuated well-known stereotypes and prejudices. We discuss the key findings in relation to current evidence on the impact of media portrayals of mental illness and stigma. Furthermore, we reflect on the ability of serious video games to promote alternative messages about mental illness and clinical practices. Future research is needed to investigate the impact that such messages have on players and to explore the role that video games can play in fostering alternative messages to reduce the stigma associated with mental illness.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".