"More than just a game, it's an app that builds awareness around Mental Health": Mental Health Stigma Reduction Using Games for Change
Bibliographic record
Abstract
Although the significance of Mental Health (MH) is becoming increasingly more accepted worldwide, the level of MH Literacy (MHL) remains low. Individuals often find it uncomfortable to discuss this crucial topic. Unwillingness to discuss MH can be a result of the stigma surrounding the topic which results in misconceptions, discrimination, and reluctance to seek help or talk openly about MH challenges. Educating individuals about MH and encouraging open discussions are key to reducing stigma. Games have proven their effectiveness for diverse learning purposes. Our goal in this paper is to assess the potential of games and digital displays for MHL education and stigma reduction by incorporating MH educational content into a mobile game with different learning strategies and a large public display to create a community. We compare the pre and post-MHL of users before and after playing our game and the results show an improvement in the MHL of participants. Our paper contributes to game design approaches by identifying mechanisms for educating individuals about MH and our results indicate that the game helps reduce stigma through the use of large displays.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".