Perceptions of COVID-19 and the Use of Health Information Technology Among People Who Are Uninsured: Multimethod Survey Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».