Perceptions of COVID-19 and the Use of Health Information Technology Among People Who Are Uninsured: Multimethod Survey Study
Bibliographic record
Abstract
BACKGROUND: As of May 2023, the novel SARS-CoV-2 has claimed nearly 7 million lives globally and >1.1 million lives in the United States. Low-income populations are often disproportionately affected by risk factors such as lifestyle, employment, and limited health literacy. These populations may lack the knowledge of appropriate infection precautions or have reduced access to care during illness, particularly in countries without universal health care. OBJECTIVE: We aimed to explore the perceptions and experiences of COVID-19, including symptoms and risk factors among uninsured individuals seeking care at a free medical clinic, and to obtain respondents' perceptions of and suggestions for adapting a mobile health (mHealth) app to an uninsured population known to have low health literacy. METHODS: We conducted a prospective multimethod survey study with a convenience sample of uninsured adults seeking care at 3 free clinics in the United States. Respondents were questioned about their risk for and awareness of COVID-19 symptoms, COVID-19 testing, current technology use, and the use of technology to facilitate their health regarding COVID-19. Data were analyzed using descriptive statistics (eg, frequencies and mean differences). In addition, a small subset of respondents from one of the clinics (n=10) participated in interviews to provide feedback about the design of a COVID-19 web-based smartphone (mHealth) app. RESULTS: The survey respondents (N=240) were 53.8% (n=129) female, were primarily White (n=113, 47.1%), and had a mean age of 50.0 (SD 11.67; range 19-72) years. Most respondents (162/222, 73%) did not think that they were at risk for COVID-19. Although respondents reported only moderate confidence in their knowledge of the short- and long-term symptoms of COVID-19, their knowledge of the symptoms aligned well with reports published by the Centers for Disease Control and Prevention of the most common acute (590/610, 96.7%) and long-term (217/271, 80.1%) symptoms. Most respondents (159/224, 71%) reported an interest in using the mHealth app to gain additional information regarding COVID-19 and available community resources. Respondents who were interviewed provided suggestions to improve the mHealth app but had overall positive perceptions about the potential usefulness and usability of the app. CONCLUSIONS: It was encouraging that the knowledge of COVID-19 symptoms aligned well with the reports published by the Centers for Disease Control and Prevention and that respondents were enthusiastic about using an mHealth app to monitor symptoms. However, it was concerning that most respondents did not think they were at a risk of contracting COVID-19.
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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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".