A content review of COVID-19-related apps used in Vietnam
Bibliographic record
Abstract
INTRODUCTION: Various digital applications (apps) have been developed as an aid to address the novel issues caused by the Coronavirus disease 2019 (COVID-19) pandemic. Vietnam has experienced a proliferation of apps for this purpose. This review aims to evaluate all Vietnamese COVID-19 apps, analyzing their features, functionality, advantages, disadvantages, and ethical issues to inform developers, communities, and governments on the most desirable features of COVID-19 apps and the user's opinions. METHODOLOGY: A systematic search was conducted on October 1, 2022, on PubMed, Scopus, Google, and the British Broadcasting Corporation (BBC) News's official website to identify COVID-19 apps available in Vietnam. The apps were evaluated through user reviews and content analysis of their specific features and drawbacks. RESULTS: Thirty Vietnam-based COVID-19 mobile apps were identified on the Apple and Google Play Store. Their functions were recorded and analyzed using a dedicated tool for appraising mobile applications. Although useful, many specific COVID-19 features were dispersed and duplicated between the apps. The most comprehensive apps still lack important functionalities, such as vaccination information. The most serious user concerns were privacy breaches during data recording and storage, technical issues, and non-user-friendly interfaces. CONCLUSIONS: The panorama of current COVID-19 apps in Vietnam is complex and includes many apps. Their overlap in features and functions could create a dispersion of mobile users that could undermine the apps' usefulness and effectiveness in combating the pandemic in Vietnam. An app that integrates the most useful features and addresses the main issues could facilitate user experience and usage uptake.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".