Virtual Care Among Adults Facing Language Barriers
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".