Playing with Perceptions: Reducing Mental Health Stigma through Proxy Experiences in Video Games
Bibliographic record
Abstract
This pilot study examines how self-identified gamers perceive video games as tools for reducing public stigma around mental health issues (MHIs) using a sequential, linked mixed-methods design. A closed online survey (N = 50) assessed demographics, gaming/media engagement, and attitudes toward MHI representation and served as the recruitment pool for an in-person qualitative phase, in which a subset completed individual playtests of Hellblade: Senua’s Sacrifice (2017) followed by semi-structured interviews (n = 7). Participants across both phases supported the use of video games for destigmatization. Playtesters emphasised that stigma-reduction impacts are more plausible when designers prioritise engaging narrative design, immersive play, and meaningful player autonomy over overt, moralizing, or didactic instruction. They linked Hellblade’s authenticity and ethical representation to the proactive collaboration between developers, mental health professionals, and people with lived experience of MHIs. They also advocated for wider consultation with related affected groups (e.g., family members) to better reflect the cumulative and far reaching societal impacts of mental health issues. Three developer-oriented recommendations emerged: define target audiences beyond “gamers” alone; design research-informed games that balance compelling play with sensitive portrayal; and disseminate across several platforms concomitantly to reach active players, wider gaming-related communities, and non-gaming publics.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".