The challenges and facilitators to successful translation and adaptation of written self-report psychological measures into sign languages: A systematic review.

Deaf people are known to have significantly poorer reading comprehension skills when compared to their hearing counterparts. This poses significant threats to text-based psychological assessments. The plethora of text-based self-report measures available provides ample opportunity to translate/adapt existing tools from text to sign language. This paper systematically reviewed the challenges and facilitators faced in previous translations/adaptations with the view to inform recommendations for future practice. This paper reports the results of a Preferred Reporting Items for Systematic Reviews and Meta-Analyses-informed systematic review of 30 studies that had translated or discussed the translation of a written self-report measure into sign language following screening against inclusion/exclusion criteria. A systematic search (powered by EbscoHost Research Database and using search terms and Boolean operators), was performed in The Allied and Complementary Medicine Database (AMED), Cinahl, Medline, APA PsycInfo, and APA PsycArticles. The Quality Assessment with Diverse Studies tool was used for quality appraisal of the included papers. Challenges/facilitators to effective translation/adaptation were grouped under linguistic, procedural, and cultural. Examples of specific linguistic, procedural, cultural challenges, and facilitators are discussed in the context of previous research and study limitations. Translating/adapting text-based self-report measures to sign language is a linguistically and procedurally demanding endeavor that requires a deep bicultural/bilingual understanding of both deaf and hearing communities. The present results and recommendations can help researchers develop suitably accessible translated/adapted self-report psychological measures and this can have significant implications on healthcare service planning and delivery. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Do Temporal Changes in Facial Expressions Help Identify Patients at Risk of Deterioration in Hospital Wards? A Post Hoc Analysis of the Visual Early Warning Score Study.

OBJECTIVES: To determine whether time-series analysis and Shannon information entropy of facial expressions predict acute clinical deterioration in patients on general hospital wards. DESIGN: Post hoc analysis of a prospective observational feasibility study (Visual Early Warning Score study). SETTING: General ward patients in a community hospital. PATIENTS: Thirty-four patients at risk of clinical deterioration. INTERVENTIONS: A 3-minute video (153,000 frames) for each of the patients enrolled into the Visual Early Warning Score study database was analyzed by a trained psychologist for facial expressions measured as action units using the Facial Action Coding System. MEASUREMENTS AND MAIN RESULTS: Three-thousand six-hundred eighty-eight action unit were analyzed over the 34 3-minute study periods. The action unit time variables considered were onset, apex, offset, and total time duration. A generalized linear regression model and time-series analyses were performed. Shannon information entropy (Hn) and diversity (Dn) were calculated from the frequency and repertoire of facial expressions. Patients subsequently admitted to critical care displayed a reduced frequency rate (95% CI moving average of the mean: 9.5-10.9 vs 26.1-28.9 in those not admitted), a higher Shannon information entropy (0.30 ± 0.06 vs 0.26 ± 0.05; p = 0.019) and diversity index (1.36 ± 0.08 vs 1.30 ± 0.07; p = 0.020) and a prolonged action unit reaction time (23.5 vs 9.4 s) compared with patients not admitted to ICU. The number of action unit identified per window within the time-series analysis predicted admission to critical care with an area under the curve of 0.88. The area under the curve for National Early Warning Score alone, Hn alone, National Early Warning Score plus Hn, and National Early Warning Score plus Hn plus Dn were 0.53, 0.75, 0.76, and 0.81, respectively. CONCLUSIONS: Patients who will be admitted to intensive care have a decrease in the number of facial expressions per unit of time and an increase in their diversity.