Asylum Seekers' Responses to Government COVID-19 Recommendations: A Cross-sectional Survey in a Swiss Canton.

Asylum seekers face multiple language, cultural and administrative barriers that could result in the inappropriate implementation of COVID-19 measures. This study aimed to explore their knowledge and attitudes to recommendations about COVID-19. We conducted a cross-sectional survey among asylum seekers living in the canton of Vaud, Switzerland. We used logistic regressions to analyze associations between knowledge about health recommendations, the experience of the pandemic and belief to rumors, and participant sociodemographic characteristics. In total, 242 people participated in the survey, with 63% of men (n = 150) and a median age of 30 years old (IQR 23-40). Low knowledge was associated with linguistic barriers (aOR 0.36, 95% CI 0.14-0.94, p = 0.028) and living in a community center (aOR 0.43, 95% CI 0.22-0.85, p = 0.014). Rejected asylum seekers were more likely to believe COVID-19 rumors (aOR 2.81, 95% CI 1.24-6.36, p = 0.013). This survey underlines the importance of tailoring health recommendations and interventions to reach asylum seekers, particularly those living in community centers or facing language barriers.

Ensemble of deep learning language models to support the creation of living systematic reviews for the COVID-19 literature.

BACKGROUND: The COVID-19 pandemic has led to an unprecedented amount of scientific publications, growing at a pace never seen before. Multiple living systematic reviews have been developed to assist professionals with up-to-date and trustworthy health information, but it is increasingly challenging for systematic reviewers to keep up with the evidence in electronic databases. We aimed to investigate deep learning-based machine learning algorithms to classify COVID-19-related publications to help scale up the epidemiological curation process. METHODS: In this retrospective study, five different pre-trained deep learning-based language models were fine-tuned on a dataset of 6365 publications manually classified into two classes, three subclasses, and 22 sub-subclasses relevant for epidemiological triage purposes. In a k-fold cross-validation setting, each standalone model was assessed on a classification task and compared against an ensemble, which takes the standalone model predictions as input and uses different strategies to infer the optimal article class. A ranking task was also considered, in which the model outputs a ranked list of sub-subclasses associated with the article. RESULTS: The ensemble model significantly outperformed the standalone classifiers, achieving a F1-score of 89.2 at the class level of the classification task. The difference between the standalone and ensemble models increases at the sub-subclass level, where the ensemble reaches a micro F1-score of 70% against 67% for the best-performing standalone model. For the ranking task, the ensemble obtained the highest recall@3, with a performance of 89%. Using an unanimity voting rule, the ensemble can provide predictions with higher confidence on a subset of the data, achieving detection of original papers with a F1-score up to 97% on a subset of 80% of the collection instead of 93% on the whole dataset. CONCLUSION: This study shows the potential of using deep learning language models to perform triage of COVID-19 references efficiently and support epidemiological curation and review. The ensemble consistently and significantly outperforms any standalone model. Fine-tuning the voting strategy thresholds is an interesting alternative to annotate a subset with higher predictive confidence.

Frequent hospital presenters' use of health information during COVID-19: results of a cross-sectional survey.

BACKGROUND: High-frequency hospital users often present with chronic and complex health conditions and are at increased risk of serious morbidity and mortality if they contract COVID-19. Understanding where high-frequency hospital users are sourcing their information, whether they understand what they find, and how they apply the information to prevent the spread of COVID-19 is essential for health authorities to be able to target communication approaches. METHODS: Cross-sectional survey of 200 frequent hospital users (115 with limited English proficiency) informed by the WHO's "Rapid, simple, flexible behavioral insights on COVID-19". Outcome measures were source of, and trust in information, and knowledge of symptoms, preventive strategies, restrictions, and identification of misinformation. RESULTS: The most frequently cited source of information was television (n = 144, 72%) followed by the internet (n = 84, 42%). One in four television users sought their information from overseas news outlets from their country of origin, while for those using the internet, 56% relied on Facebook and other forms of social media including YouTube and WeChat. Overall, 41.2% of those surveyed had inadequate knowledge about symptoms, 35.8% had inadequate knowledge about preventative strategies, 30.2% had inadequate knowledge about government-imposed restrictions, and 69% believed in misinformation. Half of the respondents (50%) trusted all information, and only one in five (20%) were uncertain or untrusting. English-speaking participants were almost three times more likely to have adequate knowledge about symptoms (OR 2.69, 95%CI 1.47;4.91) and imposed restrictions (OR 2.10 95%CI 1.06; 4.19), and 11 times more likely to recognize misinformation (OR 11.52 95%CI 5.39; 24.60) than those with limited English. CONCLUSION: Within this population of high-frequency hospital users with complex and chronic conditions, many were sourcing their information from less trustworthy or locally relevant sources, including social media and overseas news outlets. Despite this, at least half were trusting all the information that they found. Speaking a language other than English was a much greater risk factor for having inadequate knowledge about COVID-19 and believing in misinformation. Health authorities must look for methods to engage diverse communities, and tailor health messaging and education in order to reduce disparities in health outcomes.

Development of the Malay Language of understanding, attitude, practice and health literacy questionnaire on COVID-19 (MUAPHQ C-19): content validity & face validity analysis.

OBJECTIVE: This study aimed to assess the content and face validity index of the development of the understanding, attitude, practice and health literacy questionnaire on COVID-19 (MUAPHQ C-19) in the Malay language. METHODS: The development of the MUAPHQ C-19 was conducted in two stages. Stage I resulted in the generation of the instrument's items (development), and stage II resulted in the performance of the instrument's items (judgement and quantification). Six-panel experts related to the study field and ten general public participated to evaluate the validity of the MUAPHQ C-19. The content validity index (CVI), content validity ratio (CVR) and face validity index (FVI) were analysed using Microsoft Excel. RESULTS: There were 54 items and four domains, namely the understanding, attitude, practice and health literacy towards COVID-19, identified in the MUAPHQ C-19 (Version 1.0). The scale-level CVI (S-CVI/Ave) for every domain was above 0.9, which is considered acceptable. The CVR for all items was above 0.7, except for one item in the health literacy domain. Ten items were revised to improve the item's clarity, and two items were deleted due to the low CVR value and redundancy, respectively. The I-FVI exceeded the cut-off value of 0.83 except for five items from the attitude domain and four from the practice domains. Thus, seven of these items were revised to increase the clarity of items, while another two were deleted due to low I-FVI scores. Otherwise, the S-FVI/Ave for every domain exceeded the cut-off point of 0.9, which is considered acceptable. Thus, 50-item MUAPHQ C-19 (Version 3.0) was generated following the content and face validity analysis. CONCLUSIONS: The questionnaire development, content validity, and face validity process are lengthy and iterative. The assessment of the instruments' items by the content experts and the respondents is essential to guarantee the instrument's validity. Our content and face validity study has finalised the MUAPHQ C-19 version that is ready for the next phase of questionnaire validation, using Exploratory and Confirmatory Factor Analysis.

Medicine in the Era of Artificial Intelligence: Hey Chatbot, Write Me an H&P.

This Perspective envisions a world where artificial intelligence is integrated into health care.

Large Language Model as Unsupervised Health Information Retriever.

Retrieving health information is a task of search for health-related information from a variety of sources. Gathering self-reported health information may help enrich the knowledge body of the disease and its symptoms. We investigated retrieving symptom mentions in COVID-19-related Twitter posts with a pretrained large language model (GPT-3) without providing any examples (zero-shot learning). We introduced a new performance measure of total match (TM) to include exact, partial and semantic matches. Our results show that the zero-shot approach is a powerful method without the need to annotate any data, and it can assist in generating instances for few-shot learning which may achieve better performance.

Scale Up Multilingualism in Health Emergency Learning: Developing an Automated Transcription and Translation Tool.

World Health Organization's (WHO) emergency learning platform OpenWHO provided by Hasso Plattner Institut (HPI) delivered online learning in real-time and in multiple languages during the COVID-19 pandemic. The challenge was to move from manual transcription and translation to automated to increase the speed and quantity of materials and languages available. TransPipe tool was introduced to facilitate this task. We describe the TransPipe development, analyze its functioning and report key results achieved. TransPipe successfully connects existing services and provides a suitable workflow to create and maintain video subtitles in different languages. By the end of 2022, the tool transcribed nearly 4,700 minutes of video content and translated 1,050,700 characters of video subtitles. Automated transcription and translation have enormous potential as a public health learning tool, allowing the near-simultaneous availability of video subtitles on OpenWHO in many languages, thus improving the usability of the learning materials in multiple languages for wider audiences.

An exploratory analysis of pediatric anesthesia activity on Twitter using the #pedsanes hashtag.

BACKGROUND: The use of social media within the medical field has rapidly evolved over the past two decades, with Twitter being one of the most common platforms of engagement. The use of hashtags such as #pedsanes has been reported as a community builder around the subject of pediatric anesthesia. Understanding the use of #pedsanes can inform dissemination of pediatric anesthesia content and discourse. We aimed to describe the distribution and patterns of tweets and contributors using #pedsanes across the globe. METHODS: Using Tweetbinder (https://www.tweetbinder.com) and the R package "academictwitteR," we extracted tweets that included the hashtag "#pedsanes" from March 14, 2016 to March 10, 2022. Tweets were analyzed for frequency, type, unique users, impact and reach, language, content, and the most common themes. RESULTS: A total of 58 724 tweets were retrieved; 22 071 (38.8%) were original tweets including 3247 replies, while 35 971 (61.2%) were retweets all generated by over 5946 contributors located in at least 122 countries. The frequency distribution of tweets gradually increased over time with peaks in activity corresponding to major pediatric anesthesia societal meetings and during the early phases of the COVID-19 pandemic. The most retweeted and most liked posts included images. DISCUSSION: We report the widespread and increasing use of social media and the "#pedsanes" hashtag within the pediatric anesthesia and medical community over time. It remains unknown the extent to which Twitter hashtag activity translates to changes in clinical practice. However, the #pedsanes hashtag appears to play a key role in disseminating pediatric anesthesia information globally.

Factors Associated With Marshallese and Hispanic Adults' Willingness to Receive a COVID-19 Booster Dose.

INTRODUCTION/OBJECTIVES: New variants of the SARS-CoV-2 virus that causes COVID-19 will continue to develop and spread globally. The Omicron variant identified in November 2021 has many lineages. Variants spread quickly and can infect previously vaccinated individuals, prompting the Centers for Disease Control and Prevention to update vaccination recommendations. While ~230 million Americans received the initially-recommended vaccine sequence, booster uptake has been much lower; less than half of fully vaccinated individuals report receiving a booster. Racial disparities also mark patterns of COVID-19 vaccination booster uptake. This study explored willingness and motivations to get a COVID-19 booster among a diverse sample of participants. METHODS: We used convenience sampling to recruit participants 18 years of age or older who attended a community vaccine event. We conducted informal interviews during the recommended 15-min post-vaccination wait time with 55 participants who attended vaccine events at Marshallese and Hispanic community locations and comprised the recruitment pool for individual interviews. Using a qualitative descriptive design, we conducted in-depth follow-up interviews with 9 participants (Marshallese n = 5, Hispanic n = 4) to explore willingness and motivations to get boosted. We used rapid thematic template analysis to review informal interview summaries and formal interviews. The research team resolved data discrepancies by consensus. RESULTS: Participants reported high willingness to get boosted, especially if boosters were recommended in the future to protect against serious illness and mitigate the spread of COVID-19. This finding underscores how essential including recommendations to get a COVID-19 booster from trusted sources in health messaging and educational campaigns may be for increasing booster uptake. Participants described their preference for receiving future COVID-19 boosters, reporting that they would attend similar vaccine events, especially those held at faith-based organizations and facilitated by the same community partners, community health workers, and research staff. This finding shows how community engagement can overcome barriers to vaccination (ie, transportation, language, and fear of discrimination) by providing services in preferred community locations with trusted community partners. CONCLUSIONS: Findings document high willingness to get a COVID-19 booster, emphasize the role of recommendations from trusted sources in motivating booster uptake, and highlight the importance of community engagement to address disparities in vaccination coverage and reach.

The Thai version of the COVID-19 Yorkshire Rehabilitation Scale: a valid instrument for the psychometric assessment of the community members in Bangkok, Thailand.

BACKGROUND: Coronavirus disease 2019 (COVID-19) can develop into a long-term COVID in some cases, which can have a major impact on various health systems requiring appropriate treatment involving multi-disciplinary healthcare. The COVID-19 Yorkshire Rehabilitation Scale (C19-YRS) is a standardized tool widely used for screening the symptoms and severity of long-term COVID. Translation of the English version of the C19-YRS into the Thai language and testing it is essential for the psychometric evaluation of the severity of the long-term COVID syndrome prior to providing rehabilitation care for community members. METHODS: Forward-and back-translations including cross-cultural aspects were conducted in order to develop a preliminary Thai version of that tool. Five experts evaluated the content validity of the tool and produced a highly valid index. A cross-sectional study was then conducted on a sample of 337 Thai community members recovering from COVID-19. Assessment of internal consistency and individual item analyses were also performed. RESULTS: The content validity resulted in valid indices. The analyses showed that 14 items had acceptable internal consistency, based on the corrected item correlations. However, five symptom severity items and two functional ability items were deleted. The Cronbach's alpha coefficient of the final C19-YRS was 0.723, indicating acceptable internal consistency and reliability of the survey instrument. CONCLUSIONS: This study indicated that the Thai C19-YRS tool had acceptable validity and reliability for the evaluation and testing of the psychometric variables in a Thai community population. The survey instrument also had acceptable validity and reliability for screening the symptoms and severity of long-term COVID. Further studies are warranted in order to standardize the various applications of this tool.

Increasing COVID-19 Vaccination Coverage for Newcomer Communities: The Importance of Disaggregation by Language.

The COVID-19 pandemic has disproportionately affected refugee, immigrant, and migrant populations. Vaccines are essential for decreasing transmission and severity of COVID-19 infection. Understanding differences in vaccination coverage based on preferred language is crucial for focusing efforts to decrease COVID-19-related disparities. Four sites in the Minnesota Center of Excellence in Newcomer Health collaboratively evaluated completion of primary COVID-19 vaccination series on or before December 31, 2021, for patients who were 12 years or older on June 30, 2021, by preferred language. The non-English/non-Spanish speaking population included 46,714 patients who spoke 174 languages; COVID-19 vaccination coverage by language ranged from 26.2% to 88.0%. Stratifying vaccination coverage by specific language is a critical first step toward dismantling disparities and shaping interventions that best meet the needs of communities served.

Miniature Linguistic Systems for Individuals With Autism Spectrum Disorder: A Systematic Review and Meta-Analysis.

PURPOSE: Miniature linguistic systems (also known as matrix training) is a method of organizing learning targets to achieve generative learning or recombinative generalization. This systematic review is aimed at determining whether matrix training is effective for individuals with autism spectrum disorder (ASD) in terms of improving recombinative generalization for instruction-following, expressive language, play skills, and literacy skills. METHOD: A systematic review methodology was employed to limit bias in the various review stages. A multifaceted search was conducted. Potential primary studies were imported into Covidence, a systematic review software, and inclusion criteria were applied. Data were extracted regarding (a) participant characteristics, (b) matrix designs, (c) intervention methods, and (d) dependent variable. A quality appraisal using the What Works Clearinghouse (WWC) Single-Case Design Standards (Version 1.0, Pilot) was carried out. In addition to the visual analysis of the data, an effect size estimate, non-overlap of all pairs (NAP), was generated for each participant. Independent t tests and between-subjects analyses of variance were conducted to identify moderators of effectiveness. RESULTS: Twenty-six studies including 65 participants met criteria for inclusion. All included studies were single-case experimental designs. Eighteen studies received a rating of Meets Standards Without Reservations or Meets Standards With Reservations. The aggregated combined NAP scores for acquisition, recombinative generalization, and maintenance of a range of outcomes were in the high range. CONCLUSIONS: Findings suggested that matrix training is an effective teaching method for individuals with ASD for acquisition, recombinative generalization, and maintenance of a range of outcomes. Statistical analyses to identify moderators of effectiveness were insignificant. Based on the WWC Single-Case Design Standards matrix training meets criteria to be considered an evidence-based practice for individuals with ASD.

Evaluation of Preferred Language and Timing of COVID-19 Vaccine Uptake and Disease Outcomes.

Importance: Beyond traditional race and ethnicity demographic characteristics, additional discrete data variables are needed for informed health interventions in the US. Objective: To examine whether COVID-19 vaccine uptake patterns and associated disease outcomes differ among language preference groups. Design, Setting, and Participants: A cohort study of 851 410 individuals aged 18 years or older in a large multispecialty health system in Minnesota and western Wisconsin was conducted between December 15, 2020, and March 31, 2022. Exposure: Self-identified language preference and limited English proficiency (LEP) as measured by interpreter need were used to create subgroups using US census categories and attention to capture languages known to represent refugee groups. Main Outcomes and Measures: The primary outcome was COVID-19 vaccination uptake rates and time to first vaccine. Secondary outcomes were rates of COVID-19-associated hospitalization and death. Results: Most of the 851 410 participants (women, 493 910 [58.0%]; median age, 29 [IQR, 35-64] years) were US-born English speakers; 7.5% were born in other countries, 4.0% had a language preference other than English (LPOE), and 3.0% indicated LEP as measured by interpreter need. Marked temporal clusters were observed for COVID-19 vaccination uptake, hospitalizations, and deaths associated with primary series vaccine eligibility, booster availability, and COVID-19 variants. Delayed first-dose vaccine was observed with LPOE (hazard ratio [HR], 0.83; 95% CI, 0.82-0.84) and interpreter need (HR, 0.81; 95% CI, 0.80-0.82) compared with those with English language preference and proficiency. Patients with LPOE were approximately twice as likely to be hospitalized (rate ratio [RR], 1.85; 95% CI, 1.63-2.08) or die (RR, 2.13; 95% CI, 1.65-2.69). Patients with LEP experienced even higher rates of hospitalization (RR, 1.98; 95% CI, 1.73-2.25) and COVID-19-associated death (RR, 2.32; 95% CI, 1.79-2.95). Outcomes varied for individual language preference groups. Conclusions and Relevance: In this study, delayed time to first-dose vaccine was associated with increased COVID-19 hospitalization and death rates for specific LPOE and LEP groups. The findings suggest that data collection of language preference and interpreter need provides actionable health intervention information. Standardized system-level data collection, including at a national level, may improve efficient identification of social groups with disproportionate health disparities and provide key information on improving health equity in the US.

Increasing digitalization is associated with anxiety and depression: A Google Ngram analysis.

The prevalence of anxiety disorders and depression are rising worldwide. Studies investigating risk factors on a societal level leading to these rises are so far limited to social-economic status, social capital, and unemployment, while most such studies rely on self-reports to investigate these factors. Therefore, our study aims to evaluate the impact of an additional factor on a societal level, namely digitalization, by using a linguistic big data approach. We extend related work by using the Google Books Ngram Viewer (Google Ngram) to retrieve and adjust word frequencies from a large corpus of books (8 million books or 6 percent of all books ever published) and to subsequently investigate word changes in terms of anxiety disorders, depression, and digitalization. Our analyses comprise and compare data from six languages, British English, German, Spanish, Russian, French, and Italian. We also retrieved word frequencies for the control construct "religion". Our results show an increase in word frequency for anxiety, depression, and digitalization over the last 50 years (r = .79 to .89, p < .001), a significant correlation between the frequency of anxiety and depression words (r = .98, p < .001), a significant correlation between the frequency of anxiety and digitalization words (r = .81, p < .001), and a significant correlation between the frequency of depression and anxiety words (r = .81, p < .001). For the control construct religion, we found no significant correlations for word frequency over the last 50 years and no significant correlation between the frequency of anxiety and depression words. Our results showed a negative correlation between the frequency of depression and religion words (r = -.25, p < .05). We also improved the method by excluding terms with double meanings detected by 73 independent native speakers. Implications for future research and professional and clinical implications of these findings are discussed.

Communication transforms the impact of the COVID-19 pandemic on children with cancer and their families.

BACKGROUND: The COVID-19 pandemic altered healthcare systems globally, causing delays in care delivery and increased anxiety among patients and families. This study examined how hospital stakeholders and clinicians perceived the global impact of the COVID-19 pandemic on children with cancer and their families. METHODS: This secondary analysis examined data from a qualitative study consisting of 19 focus groups conducted in 8 languages throughout 16 countries. A codebook was developed with novel codes derived inductively from transcript review. In-depth analysis focused on the impact of the COVID-19 pandemic on children with cancer and their families. RESULTS: Eight themes describing the impact of the pandemic on patients and their families were identified and classified into three domains: contributing factors (COVID-19 Policies, Cancer Treatment Modifications, COVID-19 Symptoms, Beliefs), patient-related impacts (Quality of Care, Psychosocial impacts, Treatment Reluctance), and the central transformer (Communication). Participants described the ability of communication to transform the effect of contributing factors on patient-related impacts. The valence of impacts depended on the quality and quantity of communication among clinicians and between clinicians and patients and families. CONCLUSIONS: Communication served as the central factor impacting whether the COVID-19 pandemic positively or negatively affected children with cancer and families. These findings emphasize the key role communication plays in delivering patient-centered care and can guide future development of communication-centered interventions globally.

Lessons Learned From Monitoring Spanish-Language Vaccine Misinformation During the COVID-19 Pandemic.

COVID-19 vaccine misinformation is a global threat, and digital and social media support its spread. Addressing Spanish-language vaccine misinformation is critical. In 2021, we began a project to increase vaccine confidence and uptake in the United States by assessing and opposing Spanish-language COVID-19 vaccine misinformation circulating in the United States. Analysts identified trending Spanish-language vaccine misinformation each week, and trained journalists provided communications guidance for addressing the misinformation, which we delivered to community organizations via a weekly newsletter. We identified thematic and geographic trends and highlighted lessons learned to inform future efforts to monitor Spanish-language vaccine misinformation. We collected publicly available Spanish- and English-language COVID-19 vaccine misinformation across various media sources (eg, Twitter, Facebook, news, blogs). Analysts identified top trending vaccine misinformation in the Spanish query and compared it with vaccine misinformation in the English query. Analysts examined misinformation to identify its geographic source and dominant conversation themes. From September 2021 through March 2022, analysts flagged 109 pieces of trending Spanish-language COVID-19 vaccine misinformation. Through this work, we found that Spanish-language vaccine misinformation is easily identifiable. Linguistic networks are not distinct, and vaccine misinformation often circulates across English and Spanish queries. Several websites have outsized influence in promoting Spanish-language vaccine misinformation, suggesting that it may be important to focus on a handful of hyperinfluential accounts and websites. Efforts to address Spanish-language vaccine misinformation must incorporate collaboration with local communities and emphasize community building and empowerment. Ultimately, addressing Spanish-language vaccine misinformation is not an issue of data access and knowledge of how to monitor it; it is an issue of prioritization.

Subsequence and distant supervision based active learning for relation extraction of Chinese medical texts.

In recent years, relation extraction on unstructured texts has become an important task in medical research. However, relation extraction requires a large amount of labeled corpus, manually annotating sequences is time consuming and expensive. Therefore, efficient and economical methods for annotating sequences are required to ensure the performance of relational extraction. This paper proposes a method of subsequence and distant supervision based active learning. The method is annotated by selecting information-rich subsequences as a sampling unit instead of the full sentences in traditional active learning. Additionally, the method saves the labeled subsequence texts and their corresponding labels in a dictionary which is continuously updated and maintained, and pre-labels the unlabeled set through text matching based on the idea of distant supervision. Finally, the method combines a Chinese-RoBERTa-CRF model for relation extraction in Chinese medical texts. Experimental results test on the CMeIE dataset achieves the best performance compared to existing methods. And the best F1 value obtained between different sampling strategies is 55.96%.

Discovery of drugs to combat covid-19 inspired by traditional Chinese medicine.

Contributions from traditional knowledge and history have proven useful in recent years to advance drug discovery. In response to the emergence of covid-19, scientists revisited traditional Chinese medicine. This source of inspiration for drugs to treat this new disease is described here at three different levels: traditional Chinese medicinal herbs, traditional Chinese medical formulas, and traditional Chinese medical texts. Drug discovery inspired by traditional Chinese medicine still faces serious resistance for various reasons, including its system of formulas and clinical trial design. A perspective that includes related issues would benefit the reasonable application of traditional knowledge in drug research and development.