Serious Games for Improving Genetic Literacy and Genetic Risk Awareness in the General Public: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Genetic testing and genetic risk information are gaining importance in personalized medicine and disease prevention. However, progress in these fields does not reflect increased knowledge and awareness of genetic risk in the general public. OBJECTIVE: Our aim is to develop and test the efficacy of a suite of serious games, developed for mobile and Web platforms, in order to increase knowledge of basic genetic concepts and promote awareness of genetic risk management among lay people. METHODS: We developed a new ad-hoc game and modified an arcade game using mechanics suitable to explain genetic concepts. In addition, we developed an adventure game where players are immersed in virtual scenarios and manage genetic risk information to make health-related and interpersonal decisions and modulate their lifestyle. The pilot usability testing will be conducted with a convenience sample of 30 adults who will be categorized into 3 groups and assigned to one game each. Participants will be asked to report any positive or negative issues arising during the game. Subsequently, they will be asked to complete the Game Experience Questionnaire. Finally, a total of 60 teenagers and adults will be enrolled to assess knowledge transfer. Thirty participants will be assigned to the experimental group and asked to play the serious games, and 30 participants will be assigned to the control group and asked to read leaflets on the genetic concepts conveyed by the games. Participants of both groups will fill out a questionnaire before and after the intervention to assess their topic-specific knowledge of genetics. Furthermore, both groups will complete the self-efficacy questionnaire, which assesses the level of confidence in using genetic information. RESULTS: We obtained evidence of game usability in 2017. The data will be submitted to a peer-reviewed journal and used to improve the game design. Knowledge-transfer testing will begin in 2018, and we expect to collect preliminary data on the learning outcomes of serious games by December 2018. CONCLUSIONS: It is important to educate the general public about the impact of genetics and genetic testing on disease prevention and the consequent decision-making implications. Without such knowledge, individuals are more likely to make uninformed decisions or handover all decisions regarding genetic testing to their doctors. Technological innovations such as serious games might become a valid instrument to support public education and empowerment. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/9288.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.069 | 0.008 |
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".