Analysis of the Usefulness of a Serious Game to Raise Awareness about Mental Health Problems in a Sample of High School and University Students: Relationship with Familiarity and Time Spent Playing Video Games
Bibliographic record
Abstract
BACKGROUND: One of the main challenges in the field of mental health today is the stigma towards individuals who have psychological disorders. AIMS: This study aims to analyse the usefulness of applying a serious game developed for the purpose of raising awareness among students about mental health problems and analyse whether its usefulness can be influenced by the type of video games or the time that students usually devote to playing with this type of entertainment. METHOD: The serious game introduces four characters who display the symptoms of different psychological disorders. A total of 530 students participated in the study, 412 of whom comprised the experimental group and 118 the control group, 291 came from secondary school classes and 239 were university students. RESULTS: The findings show that this serious game significantly reduced total stigma among students. Variables like time habitually spent playing video games or video game preference had no bearing on the results. CONCLUSION: Our findings suggest that the serious game is an appropriated tool to reduce stigma, both in high school and university students, independently of the type of video games that young people usually play, or time spent playing video games.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".