Improving Behavioral-Based Safety Training in Using Verbal Commands Through a Theory-Driven and Feedback-Based Nonimmersive Virtual Reality Game: Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: The construction, chemical, aviation, medical, and health care industries have used serious games for safety training. To our knowledge, serious games have not been developed focusing on behavioral change to improve safety through the use of verbal commands and instilling players with heightened awareness of their spatial proximity to other people in their surroundings. OBJECTIVE: We aimed to develop a theory-driven serious game for improving safety behavior using verbal commands and validate the implementation of the theoretical frameworks used for game development. The game developed, KitchenSpeak, was a first-person character (FPC) game where users respond to in-game prompts to use loud verbal commands when they are approaching another employee's blind spot. METHODS: In addition to using the SERES framework in guiding the general game design and development, and the Reflection, Engagement, Choice, Information, Play, Exposition (RECIPE) framework to inform the design of the game mechanics, we also applied gestalt laws of perception for graphic design to guide the design of the game's user interface. We conducted 2 evaluative tests (alpha and beta) to collect end user and stakeholder feedback on the implementation of the theoretical frameworks, as well as to collect relevant information for full-scale implementation and a future validation study. RESULTS: The alpha and beta tests had 8 and 40 participants, respectively. The alpha test results revealed that the theoretical frameworks were adequately applied; however, suggestions were also made to modify and improve the game. The beta test results suggested further improvements for the game design and found no differences in the perception of ease of play between participants with and without previous FPC gaming experience (P=.47; Kruskal-Wallis). Results suggested that the game met its design and theoretical requirements, and it would be easily playable by all players regardless of their previous experience in FPC games. CONCLUSIONS: A theory-driven and evidence-based FPC game titled KitchenSpeak was developed to teach the use of kitchen-speak terms in commercial kitchens. Evaluative tests were conducted to validate the implementation of the theoretical frameworks. Our main contributions are creating and validating game-based training to improve behavioral-based safety in the workplace and the incorporation of gestalt laws of perception for graphic design in the game's user interface.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".