Designing a Serious Game (Above Water) for Stigma Reduction Surrounding Mental Health: Semistructured Interview Study With Expert Participants
Bibliographic record
Abstract
BACKGROUND: Although in many contexts unsuccessful games targeting learning, social interaction, or behavioral change have few downsides, when covering a sensitive domain such as mental health (MH), care must be taken to avoid harm and stigmatization of people who live with MH conditions. As a result, evaluation of the game to identify benefits and risks is crucial in understanding the game's success; however, assessment of these apps is often compared with the nongame control condition, resulting in findings specifically regarding entertainment value and user preferences. Research exploring the design process, integrating field experts, and guidelines for designing a successful serious game for sensitive topics is limited. OBJECTIVE: The aim of this study is to understand which elements of game design can guide a designer when designing a game for sensitive topics. METHODS: To carefully probe the design space of serious games for MH, we present Above Water (AbW), a game targeting the reduction of stigma surrounding MH, now in its second iteration. The game, AbW, serves as a consistent research probe to solicit expert feedback. Experts were recruited from a range of topic domains related to MH and wellness, game design, and user experience. RESULTS: By using this deployment as a research probe, this study demonstrates how to synthesize gained insights from multiple expert perspectives and create actionable guidelines for successful design of serious games targeting sensitive topics. CONCLUSIONS: Our work contributes to a better understanding of how to design specialized games to address sensitive topics. We present a set of guidelines for designing games for sensitive subjects, and for each guideline, we present an example of how to apply the finding to the sample game (AbW). Furthermore, we demonstrate the generalizability to other sensitive topics by providing an additional example of a game that could be designed with the presented guidelines.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".