Telemedicine Preparedness Among Older Adults With Chronic Illness: Survey of Primary Care Patients
Notice bibliographique
Résumé
BACKGROUND: Older adults are a high priority for telemedicine given their elevated COVID-19 risk and need for frequent provider contact to manage chronic illnesses. It seems that many older adults now use smartphones but few studies have examined their overall readiness for telemedicine. OBJECTIVE: The aim of this study is to survey older primary care patients about their telemedicine preparedness, including internet usage, internet-capable devices, telemedicine experiences and concerns, and perceived barriers. Results were used to inform a telemedicine preparedness training program. METHODS: Community-dwelling older adult patients (aged 65-81 years; N=30) with a chronic health condition that could be managed remotely who were present at a family medicine clinic that primarily serves an urban African American population for a prescheduled in-person appointment were asked to complete a brief survey written for this study. Data were collected February-June 2021 at a large, urban, Midwestern hospital. To minimize patient burden, the survey was limited to 10 questions, focused on the most critical topics. RESULTS: Most participants (21/30, 70%) reported having a device that could be used for telemedicine and using the internet. However, about half had only a single connected device, and messaging and video calling were the most commonly used applications. Few used email and none used online shopping or banking. Only 7 patients had had telemedicine appointments. Telemedicine users were younger than nonusers and used more internet functions than nonusers. Only 2 people reported problems with their telemedicine visits (technology and privacy). Nearly all respondents recognized avoiding travel and COVID-19 exposure as telemedicine benefits. The most common concerns were loss of the doctor-patient connection and inability to be examined. CONCLUSIONS: Most older adults reported having devices that could be used for telemedicine, but their internet use patterns did not confirm the adequacy of their devices or skills for telemedicine. Doctor-patient conversations could be helpful in addressing telemedicine concerns but device and skill gaps must be addressed as well.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».