A Game to Deal With Alcohol Abuse (Jib): Development and Game Experience Evaluation
Bibliographic record
Abstract
BACKGROUND: Alcohol abuse is the primary cause of (public) health problems in most parts of the world. However, it is undeniable that alcohol consumption is a practice that is widely accepted socially in many places, even being protected by law as a cultural and historical heritage. The issue of alcohol abuse is complex and urgent, and consequently, it is necessary to create innovative approaches such as the proposal explored in this study. OBJECTIVE: This study aimed to explore the development and evaluation of a serious game for smartphones to present a novel approach to address the issue of alcohol abuse. METHODS: A serious game was developed to instill the consequences of alcohol abuse into the player through experimentation in the game. In the game, the consequences of alcohol use are demonstrated by increasing the game speed that gives an illusion of fun but also leads to a premature death. The evaluation employed an assessment based on the Alcohol Use Disorders Identification Test (AUDIT) and the Game Experience Questionnaire (GEQ). The participants belonged to the university student's house. RESULTS: The game development process has been presented, including its mechanics and gameplay. The game has the style of action and adventure games in which the player controls an indigenous avatar that can deflect or attack opponents coming his or her way. The game evaluation comprised an assessment based on 23 participants, aged 20 to 29 years. According to the AUDIT assessment, 18 participants reported having a low or nonexistent degree of alcohol dependence and 5 declared average dependence. Regarding their habit of playing games on smartphones, 9 participants declared they have this habit of playing (habitual players), and among the 14 that did not have this habit of playing (nonhabitual players), 3 participants declared not having a smartphone at all. The GEQ core assessment showed a higher positive affect among the participants with a habit of playing games, scoring 2.80 (habitual players) on a scale of 4.0 versus 1.61 (nonhabitual players), and higher tension as an opposite relationship of 0.81 (nonhabitual players) versus 0.37 (habitual players). The overall GEQ evaluation showed that the game presents a more positive than negative affect on all users, besides showing the other desirable characteristics of serious games. CONCLUSIONS: We present a new way of dealing with the issue of alcohol abuse through a game designed for smartphones. It promotes an overall positive user experience, having a greater impact on users accustomed to games. The proposed approach has its niche, though it is still a minority in the evaluated population. Further research should explore new game features, such as new styles, to make the game more attractive to a wider audience, in addition to performing an in-depth study on the effects of playing it.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".