Design and Feasibility Study of the Mobile Application StopTheSpread
Bibliographic record
Abstract
The emergence of recent disease outbreaks calls for the design of new educational games aimed at increasing awareness in disease prevention. This article presents StopTheSpread, an educational mobile application that seeks to improve awareness about the best practices to prevent the spreading of seasonal flu in the general public. StopTheSpread integrates concepts in network science and epidemiology, within a freely available mobile application that provides a unique learning experience for free-choice learners about flu prevention. StopTheSpread teaches users basic concepts about flu prevention, within a series of games of increasing difficulty that maintain user engagement and offers a user-friendly design. StopTheSpread provides a summary of the best practices to prevent flu spreading according to the guidelines of the Centers for Disease Control and Prevention, and the World Health Organization, while connecting users to citizen science projects aimed at worldwide flu tracking. Through Facebook, Twitter, and email we reached volunteers during the COVID-19 confinement, to conduct an online feasibility study, toward assessing learning outcome in playing with our mobile application. Our results indicate that the use of StopTheSpread increased by 20% the awareness about the spreading mechanism of flu, compared with the baseline population.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".