Mosquito-Borne Arboviruses in Brazil: Assessment of Apps Based on the Mobile Apps Rating Scale (MARS)
Bibliographic record
Abstract
BACKGROUND: In Brazil, the prevalence of arboviral diseases, such as dengue, Zika, and Chikungunya, transmitted mainly by mosquitos, has increased alarmingly. In recent years, numerous free mobile apps tackling this issue have become available for various purposes and users. OBJECTIVES: This study aimed to systematically survey and evaluate these apps using the Mobile App Rating Scale (MARS). METHODS: The survey was performed on Google Play Store and sought to identify these apps adopting the descriptors “Chikungunya”, “Dengue” and “Zika”. The MARS scale was used by two researchers to evaluate the apps following their translation to Portuguese and subsequent validation. Student's T-test, Kappa statistics, and Cronbach's alpha coefficient were employed to evaluate the interobserver agreement and the reliability of the scale. RESULTS: Most apps (20 out of 29 or ~70%) were created to disseminate basic information about arboviral diseases to the population or for entertainment. There was an agreement between the two researchers for all parameters of the MARS scale, except for the engagement (p=0.002). The Cronbach's alpha coefficient indicated good reliability. CONCLUSIONS: The use of the MARS scale has shown that most of the evaluated apps were developed to share information about arboviral diseases in an interactive way, but they do not necessarily have the purpose of influencing their users to change behaviours related to vector control or the prevention of arboviral diseases, which the authors feel would be a more appropriate aim for future app development.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".