Uptake and Utilization of the COVID-19 Alert App within a University Community in New York
Bibliographic record
Abstract
The rapid onset of the COVID-19 pandemic elicited a swift response to control the virus ubiquitous within the United States. Expanded telehealth and health informatics became critical components of the pandemic response. The aim of this study was to assess the utilization of the COVID-19 New York (NY) Alert App and identify the perceived benefits and limitations of the App. A cross-sectional design was employed to collect data by using questionnaires with closed-ended and open-ended questions. The survey was developed and administered during March through April 2021. The study found that the highest rated benefit from using the COVID-19 NY Alert App was receiving alerts about being in close proximity to individuals diagnosed with COVID-19. Results showed that ineffective (insufficient and inappropriate) usage was the highest rated potential challenge for using the App. Study subjects were likely to download this Alert App when they perceived more benefits and less barriers to using the App. Findings from this study can help improve utilization of the App and inform development of similar tele-education tools. The study illuminated considerations for health information applications in scaling-up traditional COVID-19 tracing efforts and may facilitate the design of similar emergency preparedness health technology.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".