Effectiveness of the CoronaCheck Mobile Health Application: An Analytical Cross-sectional Study in Lower–Middle-Income Countries
Bibliographic record
Abstract
In low- and middle-income countries (LMICs), digital health has repeatedly proven to play a promising role in optimizing the level of care available and accessible to vulnerable populations in a timely and cost-effective manner. Despite financial constraints, digital technologies have demonstrated unique potential in reaching typically inaccessible groups, including refugee and internally displaced populations. Given the potential of digital health to bridge gaps in healthcare coverage, enhance quality, and improve affordability, this study aimed to assess the effectiveness of the CoronaCheck mHealth application among people in LMICs. This analytical cross-sectional study spanned multiple LMICs, focusing on males and females aged 18 years and older in regions such as Pakistan, Afghanistan, Kenya, and Tajikistan. Participants were selected through convenient sampling. Knowledge change and self-reported behavior change were assessed. The p -value of <0.05 was considered statistically significant. A total of 1507 participants responded to the survey. A difference in knowledge among countries was observed with a statistically significant p -value of <0.001. An effect modification was observed between gender and refugees/migrants with a statistically significant p -value of <0.1. A substantial self-reported behavior change was identified among those residing in informal settlements with a significant p -value of <0.001. Moreover, the users expressed a high level of satisfaction with the application. The CoronaCheck application has demonstrated its effectiveness in promoting both knowledge change and self-reported behavior change among people in LMICs. The user-friendly nature of the application, coupled with its accessibility at no cost, represents an asset in promoting health education and awareness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".