Virtual Care Among Adults Facing Language Barriers
Notice bibliographique
Résumé
Importance: Patients with language barriers may experience difficulties accessing virtual care. Objectives: To synthesize the literature on the association between language barriers and use of and satisfaction with virtual care among adult patients and among caregivers of pediatric patients in high-income countries. Data Sources: Four electronic databases, MEDLINE, Embase, PsycINFO, and Web of Science, were searched from inception to March 2023 using a combination of language and virtual care terms. Study Selection: Eligible studies compared quantitative data on use of or satisfaction with virtual care among adult patients or caregivers of pediatric patients with or without language barriers in high-income countries. Data Extraction and Synthesis: Data were extracted by 2 independent reviewers using a piloted data extraction form. Risk of bias assessments were performed using the ROBINS-E tool. Data were synthesized by outcome type using random-effects meta-analyses stratified by primary vs specialist care and narrative synthesis. Reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses 2020 guideline. Main Outcomes and Measures: The primary outcomes were use of virtual care and satisfaction with virtual care among adult patients and among caregivers of pediatric patients with vs without language barriers. Results: In total, 41 studies were included, 35 with adult patients (N = 4 543 906) and 6 with caregivers of pediatric patients (N = 7921). Most studies (n = 38) were set in the US, and examined use of virtual care (n = 35). Adult patients facing language barriers had no significant difference in adjusted pooled odds of virtual vs in-person primary or specialist care use (adjusted OR [AOR], 0.91 [95% CI, 0.61-1.35]; n = 5; I2 = 95.0%) and significantly lower adjusted pooled odds of video vs telephone primary or specialist care use (AOR, 0.66 [95% CI, 0.52-0.85]; n = 5, I2 = 93.5%), with high heterogeneity. Restricting to specialist care, adult patients facing language barriers had significantly lower adjusted pooled odds of using virtual vs in-person (AOR, 0.78 [95% CI, 0.70-0.87]; n = 4; I2 = 0.0%) and video vs telephone (AOR, 0.62 [95% CI, 0.53-0.73]; n = 3; I2 = 0.0%) care, with low heterogeneity. Results for caregivers of pediatric patients were limited and showed no significant difference in odds of virtual vs in-person specialist care, with a wide confidence interval and high heterogeneity (OR, 0.62 [95% CI, 0.38-1.02]; n = 3; I2 = 91.2%). Results specific to primary care and results on other use of virtual care outcomes (eg, visit noncompletion) were inconclusive due to limited numbers of studies (eg, n = 2 studies reporting AOR of visit noncompletion for primary and specialist care, with I2 = 75.5% and 89.6%, respectively, for the corresponding meta-analyses). Results on satisfaction with virtual care, synthesized narratively, were limited and mixed. In studies of adult patients and caregivers of pediatric patients (n = 3 each), 2 of the 3 studies found no statistically significant difference in satisfaction, while 1 study found significantly lower satisfaction. Conclusions and Relevance: In this systematic review and meta-analysis, adult patients facing language barriers had no significant difference in pooled odds of using virtual compared with in-person care overall, and significantly lower pooled odds of using video compared to telephone care overall. Restricting to specialist care, pooled odds of using virtual care and video care were significantly lower among adult patients facing language barriers, with low heterogeneity. Further research on virtual care among individuals facing language barriers is needed, focusing on virtual primary care, patient satisfaction, and caregivers of pediatric patients.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 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 ».