Virtual Care Prior to and During COVID-19: Cross-sectional Survey of Rural and Urban Adults
Notice bibliographique
Résumé
BACKGROUND: To reduce person-to-person contact, the COVID-19 pandemic has driven a massive shift to virtual care. Defined as the use of technology (synchronous or asynchronous) to support communication between health care providers and patients, rural-urban differences in virtual care are relatively unexplored. OBJECTIVE: The 2-fold purpose of this study was to examine rural and urban virtual care access, use, and satisfaction during the pandemic and to identify any unmet needs. METHODS: This study was a cross-sectional online survey exploring virtual care among rural and urban adults in summer 2021 using a combination of fixed and open-ended response options. Quantitative data were analyzed using both descriptive and inferential statistics, and qualitative data were analyzed using inductive thematic content analysis. RESULTS: Overall, 501 (373, 74.4% female; age range 19-86 years; 237, 47.3% rural-living) Western Canadians completed the survey. Virtual care use was high among both rural (171/237, 72.2%) and urban (188/264, 71.2%) participants, with over one-half (279/501, 55.7%) reporting having only started to use virtual care since the pandemic. The self-reported need for mental health programs and services increased during the pandemic, compared with prior for both rural and urban participants. Among virtual care users, interest in its continuation was high. Our analysis also shows that internet quality (all P<.05) and eHealth literacy (all P<.001) were positively associated with participants' perceptions of virtual care usefulness, ease of use, and satisfaction, with no rural-urban differences. Rural participants were less likely to have used video in communicating with doctors or health care providers, compared with urban participants (P<.001). When describing unmet needs, participants described a (1) lack of access to care, (2) limited health promotion and prevention options, and (3) lack of mental health service options. CONCLUSIONS: The increased demand for and use of virtual care may reflect increased availability and a lack of alternatives due to limited in-person services during the COVID-19 pandemic, so a balance between virtual care and in-person care is important to consider postpandemic. Further, ensuring availability of high-speed internet and education to support patients will be important for providing accessible and effective virtual care, especially for rural residents.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».