Interpreter use in sustained nurse home visiting: interpreter experience and support.

BACKGROUND: The aim of this study was to explore the experiences of healthcare interpreters working with child and family health nurses (CFHNs) in providing child and family health nursing (CFHN) services and sustained nurse home visiting (SNHV) programs to culturally and linguistically diverse (CALD) families with limited English proficiency. METHODS: A mixed methods longitudinal research design was conducted to develop, implement and evaluate a training and practice support model for healthcare interpreters working with nurses and CALD families in providing CFHN services and SNHV programs in three major local health services in Sydney, Australia. One pre-training survey with 24 healthcare interpreters was conducted; field notes were recorded during training and implementation; and a post-implementation focus group with six healthcare interpreters was conducted. Quantitative survey data were analysed descriptively using Alchemer. The focus group was audio-recorded for transcription purposes, and this and the field notes were thematically analysed applying a socioecological framework. RESULTS: Three themes were identified from the initial, pre-training survey: facilitate communication and delivery accurately; a bridge linking the clients and the healthcare practitioners; and make everybody feel comfortable. Practice support implementation was negatively impact by system and COVID-19 related barriers. Four themes were developed from evaluative phase of the study including: system-related issues; interpreters' challenges; working with nurses; and client session related issues. CONCLUSION: Quality interpreting was favourably influenced by adequate time for interpreting the session including a pre- and post-briefing session with CFHNs, an appropriate mode of interpretation, allocation of female interpreters and the same interpreters with CALD mothers and clarity about interpreter role and cultural comfort. These strategies support the quality of communication and relationships in delivery of CFHN services and SNHV programs to CALD mothers with limited English proficiency.

Meeting them where they are on the web: addressing structural barriers for Latinos in telehealth care.

As we enter an era of health care that incorporates telehealth for routine provision of care, we can build a system that consciously and proactively includes vulnerable patients, thereby avoiding further exacerbation of health disparities. A practical way to reach out to Latino patients is to use media they already widely use. Rather than expect patients to adapt to suboptimal systems of telehealth care, we can improve telehealth for Latinos by using platforms already familiar to them and thereby refocus telehealth delivery systems to provide patient-centered care. Such care is responsive to patients' needs and preferences; for Latinos, this includes using digital devices that they actually own (ie, smartphones). Equity-centered telehealth is accessible for all, regardless of linguistic, literacy, and socioeconomic barriers.

Spanish-Language Communication of COVID-19 Information Across US Local Health Department Websites.

The COVID-19 pandemic has brought new urgency to a longstanding problem: the US health system is not well-equipped to accommodate the country's large limited English proficient (LEP) population in times of national emergency. We examined the landscape of Spanish-language COVID-19 website information compared to information in English provided by health departments of the top 10 cities by population in the USA. For each city, coders evaluated three score measures (amount of information, presentation quality, and ease of navigation) for six content types (general information, symptoms, testing, prevention, vaccines, and live statistics) across six delivery modes (print resources, website text, videos, external links, data visualization, and media toolkits). We then calculated a grand average, combining all cities' values per score measure for each content type-delivery mode combination, to understand the landscape of Spanish-language information across the country. Overall, we found that, for all cities combined, nearly all content types and delivery modes in Spanish were inferior or non-existent compared to English resources. Our findings also showed much variability and spread concerning content type and delivery mode of information. Finally, our findings uncovered three main clusters of content type and delivery mode combinations for Spanish-language information, ranging from similar to worse, compared to information in English. Our findings suggest that COVID-19 information was not equivalently provided in Spanish, despite federal guidance regarding language access during times of national emergency. These results can inform ongoing and future emergency communication plans for Spanish-preferring LEP and other LEP populations in the USA.

Analysis of Language Translations of State Governments' Coronavirus Disease 2019 Vaccine Websites.

Introduction: During the coronavirus disease 2019 (COVID-19) vaccination campaign, non-English-communicating individuals have faced inequities in access to resources for vaccine education and uptake. We characterized the language translation status of states' COVID-19 vaccine websites to inform discussion on the sufficiency of translated information and strategies for expanding the availability of multilingual vaccine information. Methods: We identified the primary COVID-19 vaccine website for all 50 states, the District of Columbia, and the federal government ("jurisdictions") and determined the languages into which information about obtaining the vaccine (access) and vaccine safety and efficacy had been translated, as of October 2021. We compared these findings with data from the American Community Survey to determine how many individuals had these online resources available in their primary language. Results: Only 56% of jurisdictions provided professionally translated information about COVID-19 vaccine safety and efficacy, and only 50% provided professionally translated information about how to register for or obtain the COVID-19 vaccine, in at least one language. Consequently, ∼26 million Americans may not have accurate vaccine safety and efficacy information available, and ∼29 million Americans may not have vaccine access information available, from their jurisdiction in their primary language. Furthermore, translated information often was limited in scope and/or number of languages provided. Conclusion: Translation of COVID-19 vaccine information on state government websites currently is insufficient to meet the needs of non-English-communicating populations. This analysis can inform discussions about resource needs and operational considerations for adequate provision of multilingual, critical health information.

The Use of Automated Machine Translation to Translate Figurative Language in a Clinical Setting: Analysis of a Convenience Sample of Patients Drawn From a Randomized Controlled Trial.

BACKGROUND: Patients with limited English proficiency frequently receive substandard health care. Asynchronous telepsychiatry (ATP) has been established as a clinically valid method for psychiatric assessments. The addition of automated speech recognition (ASR) and automated machine translation (AMT) technologies to asynchronous telepsychiatry may be a viable artificial intelligence (AI)-language interpretation option. OBJECTIVE: This project measures the frequency and accuracy of the translation of figurative language devices (FLDs) and patient word count per minute, in a subset of psychiatric interviews from a larger trial, as an approximation to patient speech complexity and quantity in clinical encounters that require interpretation. METHODS: A total of 6 patients were selected from the original trial, where they had undergone 2 assessments, once by an English-speaking psychiatrist through a Spanish-speaking human interpreter and once in Spanish by a trained mental health interviewer-researcher with AI interpretation. 3 (50%) of the 6 selected patients were interviewed via videoconferencing because of the COVID-19 pandemic. Interview transcripts were created by automated speech recognition with manual corrections for transcriptional accuracy and assessment for translational accuracy of FLDs. RESULTS: AI-interpreted interviews were found to have a significant increase in the use of FLDs and patient word count per minute. Both human and AI-interpreted FLDs were frequently translated inaccurately, however FLD translation may be more accurate on videoconferencing. CONCLUSIONS: AI interpretation is currently not sufficiently accurate for use in clinical settings. However, this study suggests that alternatives to human interpretation are needed to circumvent modifications to patients' speech. While AI interpretation technologies are being further developed, using videoconferencing for human interpreting may be more accurate than in-person interpreting. TRIAL REGISTRATION: ClinicalTrials.gov NCT03538860; https://clinicaltrials.gov/ct2/show/NCT03538860.

Assessing Medical Student Readiness to Navigate Language Barriers in Telehealth: Cross-sectional Survey Study.

BACKGROUND: The COVID-19 pandemic has greatly increased telehealth usage in the United States. Patients with limited English proficiency (LEP) face barriers to health care, which may be mitigated when providers work with professional interpreters. However, telehealth may exacerbate disparities if clinicians are not trained to work with interpreters in that setting. Although medical students are now involved in telehealth on an unprecedented scale, no educational innovations have been published that focus on digital care across language barriers. OBJECTIVE: The aim of this study is to investigate advanced medical students' confidence in caring for patients with LEP during telehealth encounters. METHODS: We administered a written survey to medical students on clinical clerkships at one US institution in August and September 2020. We assessed students' overall confidence in working with interpreters; confidence in performing 8 clinical tasks during in-person versus telehealth encounters; and frequency of performing 5 different clinical tasks with patients with LEP compared to English-speaking patients during in-person versus telehealth encounters. Wilcoxon signed-rank tests and chi-square tests were used to compare confidence and task performance frequency, respectively, for patients with LEP versus English-speaking patients during telehealth encounters. Students were also asked to identify barriers to care for patients with LEP. The free-response questions were qualitatively analyzed using open coding to identify key themes. RESULTS: Of 300 medical students surveyed, 121 responded. Furthermore, 72 students answered >50% of questions and were included in the analyses. Compared to caring for patients with LEP during in-person encounters, respondents were less confident in working with interpreters (P<.001), developing trust (P<.001), identifying agenda (P=.005), eliciting preferences for diabetes management (P=.01), and empowering patients in lifestyle modifications (P=.04) during telehealth encounters. During both in-person and telehealth encounters, approximately half of students (40%-78%) reported engaging less frequently in every clinical task with patients with LEP and this was as low as 22% (13/59) for some tasks. Students identified these key barriers to care for patients with LEP: time pressure, interpretation quality and access, technical difficulties, cultural differences, and difficulty with rapport building. CONCLUSIONS: Advanced medical students were significantly less confident caring for patients with LEP via telehealth than in person. Broader implementation of training around navigating language barriers is necessary for telehealth care, which has rapidly expanded in the United States. Our study identified potential key areas for curricular focus, including creating patient-centered agendas and management plans within the constraints of virtual settings. These developments must take place simultaneously with systems-level improvements in interpreter infrastructure to ensure high-quality care for linguistically diverse patients.