Needs for Successful Engagement in Telemedicine Among Rural Older US Veterans and Their Caregivers: Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: Telemedicine is an important option for rural older adults who often must travel far distances to clinics or forgo essential care. In 2014, the Geriatric Research, Education, and Clinical Centers (GRECC) of the US Veterans Health Administration (VA) established a national telemedicine network called GRECC Connect. This network increased access to geriatric specialty care for the 1.4 million rural VA-enrolled veterans aged 65 years or older. The use of telemedicine skyrocketed during the COVID-19 pandemic, which disproportionately impacted older adults, exacerbating disparities in specialty care access as overburdened systems shut down in-person services. This surge presented a unique opportunity to study the supports necessary for those who would forgo telemedicine if in-person care were available. OBJECTIVE: In spring 2021, we interviewed veterans and their informal caregivers to (1) elicit their experiences attempting to prepare for a video visit with a GRECC Connect geriatric specialist and (2) explore facilitators and barriers to successful engagement in a telemedicine visit. METHODS: We conducted a cross-sectional qualitative evaluation with patients and their caregivers who agreed to participate in at least 1 GRECC Connect telemedicine visit in the previous 3 months. A total of 30 participants from 6 geographically diverse GRECC Connect hub sites agreed to participate. Semistructured interviews were conducted through telephone or the VA's videoconference platform for home telemedicine visits (VA Video Connect) per participant preference. We observed challenges and, when needed, provided real-time technical support to facilitate VA Video Connect use for interviews. All interviews were recorded with permission and professionally transcribed. A team of 5 researchers experienced in qualitative research analyzed interview transcripts using rapid qualitative analysis. RESULTS: From 30 participant interviews, we identified the following 4 categories of supports participants described regarding successful engagement in telemedicine, as defined by visit completion, satisfaction, and willingness to engage in telemedicine in the future: (1) caregiver presence to facilitate technology setup and communication; (2) flexibility in visit modality (eg, video from home or a clinic or telephone); (3) technology support (eg, determining device compatibility or providing instruction and on-demand assistance); and (4) assurance of comfort with web-based communication, including orientation to features like closed captioning. Supports were needed at multiple points before the visit, and participants stressed the importance of eliciting the varying needs and preferences of each patient-caregiver dyad. Though many initially agreed to a telemedicine visit because of pandemic-related clinic closures, participants were satisfied with telemedicine and willing to use it for other types of health care visits. CONCLUSIONS: To close gaps in telemedicine use among rural older adults, supports must be tailored to individuals, accounting for technology availability and comfort, as well as availability of and need for caregiver involvement. Comprehensive scaffolding of support starts well before the first telemedicine visit.
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 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 | 0,010 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,009 | 0,005 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».