Language Interpretation Research Articles

LSC50: Colombian Sign Language Video and Inertial Measurement dataset

In Colombia, approximately more than 500.000 people have disabling hearing loss, representing around 1% of the population in Colombia, and only 400 professional interpreters of Colombian Sign Language (LSC) are available in the whole country. In this data descriptor, we present a dataset with recordings of Colombian Sign Language (LSC) as an important effort to develop technologies that facilitate social inclusion and equity for the deaf community in Colombia. This dataset includes multimodal recordings of native and non-native signers signing different elements of the LSC lexicon. In particular, we provide recordings of video and inertial measurement units, along with detected body landmarks and computed information on the inverse kinematics of the upper body such as flexion, extension, and rotation of arms and wrists. This dataset is the first of its kind for LSC and it is key for the development of technologies needed to better understand the language and reduce communication barriers between people with and without hearing loss.

A real-time platform for Spanish Sign Language interpretation

AbstractMore than 13% of the Spanish population suffers from hearing impairment. Despite the existence of several hearing aids and implants making sounds clearer and louder, there are some people unable to use them and, consequently, their only means of communication is Sign Language. However, this language is not widely known in the society. Consequently, those who are deaf or hard of hearing may be socially excluded and experience frustration as a result of the lack of communication. In this context, Sign Language Recognition and interpretation would help to break down the existing communication barriers and facilitate the creation of inclusive environments. With that aim, this paper presents a real-time platform to recognize and interpret finger-spelt words in Spanish Sign Language (Lengua de Signos Española). As finger spelling implies the recognition of each signed letter, a comparative analysis of different deep learning techniques to properly recognize the Spanish Sign Language alphabet has been carried out. For that, due to the lack of Spanish Sign Language datasets, the first step was to capture and build an image dataset representing its 30 letters. As there are static and in-motion letters, spatial and temporal analysis has been conducted by considering different kind of neural networks (Convolutional Neural Networks, Recurrent Neural Networks, and Vision Transformers). The experimental results highlight the good performance of the studied architectures, obtaining a maximum accuracy of 79.96% on previously unseen data. Finally, a real-time platform for the recognition and interpretation of finger-spelt words in Spanish Sign Language has been implemented making the communication possible.

Development and validation of a fluency rating scale for Swiss German Sign Language

IntroductionSign language fluency is an area that has received very little attention within research on sign language education and assessment. Therefore, we wanted to develop and validate a rating scale of fluency for Swiss German Sign Language (Deutschschweizerische Gebärdensprache, DSGS).MethodsDifferent kinds of data were collected to inform the rating scale development. The data were from (1) focus group interviews with sign language teachers (N = 3); (2) annotated DSGS data from users/learners with various levels of proficiency (i.e., deaf native signers of DSGS, hearing sign language interpreters, and beginning learners of DSGS, approximately CEFR level A1-A2) (N = 28) who completed different signing tasks that were manipulated by preparation time; (3) feedback from raters (N = 3); and (4) complimented with theory from spoken and sign language fluency.ResultsIn the focus group interview, sign language teachers identified a number of fluency aspects. The annotated DSGS data were analyzed using different regression models to see how language background and preparation time for the tasks can predict aspects of fluency (e.g., number and duration of pauses). Whereas preparation time showed only a slight effect in the annotated data, language background predicted the occurrence of fluency features that also informed the scale development. The resulting rating scale consisted of six criteria, each on a six-point scale. DSGS performances (N = 162) (same as the annotated data) from the different groups of DSGS users/learners were rated by three raters. The rated data were analyzed using multi-facet Rasch measurement. Overall, the rating scale functioned well, with each score category being modal at some point on the continuum. Results from correlation and regression analysis of the annotated data and rated DSGS performances complemented validity evidence of the rating scale.DiscussionWe argue that the different sources of data serve as a sound empirical basis for the operationalized “DSGS fluency construct” in the rating scale. The results of the analyses relating performance data to ratings show strong validity evidence of the second version of the rating scale. Together, the objective fluency measures explained 88% of the variance in the rating scores.

Video Remote Sign Language Interpreting in Health Communication for Deaf People: Protocol for a Randomized Controlled Trial.

The current standard of interpretation provision is not efficacious or not acceptable to Deaf patients who communicate using sign language. In-person or video relay interpretation (VRI) sign language interpretation is largely unavailable. There are no clear data on the availability of VRI or in-person interpretation. Given the limited number of available sign language interpreters and the cost, VRI may be more available than in-person. Existing evidence tends to focus on assessing personal preferences of Deaf users regarding interpretation and interpreters' preferences. Although respecting preferences is essential, there is a vacuum of knowledge on how the format of access to interpretation impacts the quality of communication between Deaf persons and health personnel. This study aims to look at the effectiveness of the VRI system in improving communication outcomes between Deaf patients and doctors versus the available standard of care of the usual communication tools, including informal interpretation, lip- or note-reading, and using their mobile phones to contact a formal or informal interpreter, for Deaf patients aged 18 years and older in Bogota, Colombia. This is a randomized controlled trial with a total sample size of 216 participants, divided into 2 groups: an intervention group, which receives a medical appointment using VRI, and a control group, which receives a medical appointment using standard communication. Both the Deaf participants and the health care professionals will be blinded to the allocation, as they will not know whether the appointment will involve VRI or standard communication until they arrive at the office. The primary outcome measure will be an assessment of communication using a Doctor-Patient Communication Scale. This scale was translated into Colombian Sign Language following a rigorous cultural adaptation and translation procedure. Furthermore, the database contains key clinical variables and recommendations provided by the doctor during a general medicine appointment. We will compute associations. Recruitment opened on August 24, 2023. As of July 2024, 180 participants had been enrolled. The intervention and data collection were finalized in October 2024. The findings of this study are expected to be submitted for publication in early 2025. This study will provide rigorous evidence regarding information and communications technology intervention in health care, addressing empirical challenges in using inclusive research designs in public health. In addition, effective VRI models that address the challenges faced by Deaf people will be tested, implemented, and maintained in low- and middle-income countries. A disability-inclusive evaluative tool for quality communication mediated by VRI in health care is also tested. Ultimately, this will lead to evidence-based recommendations for implementing the Convention on the Rights of Persons with Disabilities (CRPD) in mobile health contexts. ClinicalTrials.gov NCT05966623; https://clinicaltrials.gov/study/NCT05966623. DERR1-10.2196/64590.

형사사법(司法)절차에서 통역을 받을 권리의 헌법적 보장과 그 실현을 위한 입법과제

This paper explores the critical role of judiciary interpreting in legal proceedings, focusing on the necessity for accurate and effective court interpretation for defendants who do not speak the court's language. Judiciary interpreting is distinct from general interpreting because it directly impacts the protection of basic rights and the fairness of the trial. Without proper interpretation, defendants may not fully understand the charges against them or the legal procedures, leading to the potential infringement of their defense rights and the integrity of the trial. As South Korea transitions into a multicultural society, the influx of immigrants has led to an increase in criminal cases involving non-native Korean speakers. These defendants often face difficulties in both testimony and communication with legal professionals due to language barriers. In response, South Korean law mandates the assistance of interpreters during criminal procedures (Article 180 of the Criminal Procedure Act) and has introduced systems like the 2021 court interpreter certification program. However, issues such as a lack of qualified interpreters for minority languages and insufficient understanding of legal terminology among interpreters still hinder the full realization of defendants' rights. This paper addresses the constitutional foundation of the right to interpretation, emphasizing that this right derives from the principles of due process, the right to a fair trial, and bodily freedom guaranteed in the Korean Constitution. By comparing legal systems in Canada, the U.S., and the European Court of Human Rights (ECHR), this study examines the standards for interpretation rights and suggests improvements for South Korea's judiciary interpreting system. Key recommendations include establishing objective criteria for determining when interpretation is necessary, ensuring interpreter competence, and formalizing interpretation guidelines in the legal system to safeguard the accuracy and impartiality of court interpretation. The paper concludes that an effective judiciary interpreting system is essential to maintaining the fairness of trials in a multicultural society and calls for legislative reforms to enhance interpreter training and certification.

The ASL Dataset for Real-Time Recognition and Integration with LLM Services

This study aims to investigate the impact of hand gesture recognition techniques on the efficiency of American Sign Language (ASL) interpretation, addressing a critical gap in the existing literature. The research seeks new insights into the challenges of automated sign language recognition, contributing to a deeper understanding of accessibility in communication for the deaf and hard-of-hearing community. The study employs a quantitative approach, using a dataset comprising hand gesture images representing the static letters of the ASL alphabet collected from multiple users. Data were collected from various individuals to ensure diversity and analyzed using machine learning models to evaluate their effectiveness in recognizing ASL signs. The results reveal that the machine learning models implemented achieved a high accuracy rate in recognizing hand gestures, indicating that person-specific variations do not significantly hinder performance. These findings provide evidence that the proposed dataset and methodologies can improve the reliability of sign language recognition systems, offering significant implications for the development of more inclusive communication technologies. This research offers a novel perspective on sign language recognition, providing valuable insight that extends the current understanding of gesture-based communication systems. The study’s findings contribute to advancements in accessibility technologies, highlighting areas for future research and practical applications in improving communication for the Deaf and hard of hearing community.

Language interpretation and translation in emergency care: A scoping review protocol.

Patients with preferred languages other than English face barriers to communication and access to appropriate care in English-speaking emergency care systems, leading to poorer communication and quality of care, as well as increased rates of investigations and healthcare utilization. While professional interpretation can help bridge this gap, uptake is exceedingly poor, suggesting the need for enhanced implementation and more accessible modalities. Our study will map the existing literature on interpretation/translation in emergency care, with a focus on the breadth of modalities, barriers/facilitators to implementation, and effectiveness/implementation outcomes. We will conduct a scoping review based on the Joanna Briggs Institute methodology. We will search MEDLINE, Embase, PsycINFO, CINAHL, Scopus, iPortal, Native Health Database and Cochrane Library CENTRAL for articles from inception to May 2024 without any language or country restrictions. Primary research articles involving interpretation/translation between English and a non-English language during emergency healthcare encounters will be included. Screening and data extraction will be completed by two independent team members. Results will be descriptively summarized and barriers/facilitators to implementation will be mapped according to the Consolidated Framework for Implementation Research. Results will be disseminated at academic conferences and published in a peer-reviewed journal. We will share our key findings via a graphical abstract and social media campaign. Our team includes our provincial health authority interpretation services lead who brings lived experience and will inform and validate our results and help identify future areas of needed research. They will also help us identify key messages and appropriate methods for dissemination to maximize knowledge translation to patients/families, local policy/clinical practice, as well as funding agencies.

Sign language interpretation using machine learning and artificial intelligence

AbstractSign language is the only way for deaf and mute people to represent their needs and feelings. Most of non-deaf-mute people do not understand sign language, which leads to many difficulties for deaf-mutes' communication in their social life. Sign language interpretation systems and applications get a lot of attention in the recent years. In this paper, we review sign language recognition and interpretation studies based on machine learning, image processing, artificial intelligence, and animation tools. The two reverse processes for sign language interpretation are illustrated. This study discusses the recent research on sign language translation to text and speech with the help of hand gestures, facial expressions interpretation, and lip reading. Also, state of the art in speech to sign language translation is discussed. In addition, some of the popular and highly rated Android and Apple mobile applications that facilitate disabled people communication are presented. This paper clarifies and highlights the recent research and real used applications for deaf-mute people help. This paper tries to provide a link between research proposals and real applications. This link can help covering any gap or non-handled functionalities in the real used applications. Based on our study, we introduce a proposal involves set of functionalities/options that separately introduced and discussed by the recent research studies. These recent research directions should be integrated for achieving more real help. Also, a set of non-addressed research directions are suggested for future focus.

The Development of Intercultural Competence for Teachers of English as a Foreign Language through an Instructional Design Project

Most teaching pedagogies in English as a Foreign Language (EFL) classes prioritize linguistic skills development over exploring how cultural factors shape language interpretation. To address the gap, this study developed and evaluated an instructional design project using the attention, relevance, confidence, and satisfaction (ARCS) motivational design model combined with critical pedagogy through computer-mediated communication (CMC) tools. The purpose of this research is to enhance EFL teachers’ intercultural competence. Multimedia resources were used to capture participants’ attention, and news articles were selected to connect to their lived experiences. Online forums were used to build their confidence, and intercultural activities provided satisfaction through practical engagement. Critical literacy pedagogy guided the design of questions to encourage EFL teachers to explore assumptions, challenge perspectives, and critically analyze language. Data from 16 EFL teachers’ questionnaires, online comments, and interviews indicated that task attractiveness and the online environment motivated EFL teachers to develop critical literacy skills. Future studies could explore additional strategies for integrating intercultural competence into EFL teaching and examine how cultural dynamics within groups influence online communication.

Concept model and realization of object orientied system with metaprogramming

The purpose of the research is to develop a conceptual model of an object oriented system and implement it using a functional language with metaprogramming support. Methods. A conceptual model of an object oriented system was developed based on a dynamic object system with message passing and dynamic dispatch. The developed object oriented system includes functions for creating and deleting classes, creating and deleting object instances of a class, functions for working with object properties, functions for adding methods to a class and passing messages. Inheritance is also possible, when a child class will have the properties of the parent class and its own properties. Results. The object oriented system was implemented using a set of macros and helper functions. The basic data structure was a hash table with key access capabilities, which was also implemented using macros. When a class is created, an instance constructor is automatically generated, setting the initial values of the object’s fields. Inheritance is implemented as the inclusion of fields of a parent class in a child object. Polymorphism is implemented as dynamic dispatch: when processing a message, a handler method is searched. The resulting object oriented system, together with the hash table module, occupies 97 lines. Conclusion. As a result of the work, a conceptual model of an object oriented system was created and implemented, which includes the main functions of the object oriented programming paradigm. It has been shown that macros can be used to create a compact object oriented system without the need to add any new functions or capabilities to the functional language interpreter.

At risk but not adequately included: People with disabilities' experience of COVID-19 in Zambia.

COVID-19 had an impact on all sections of society, including people with disabilities. The authors aimed to explore the needs and experiences of people with disabilities in Zambia during the COVID-19 pandemic. In this hermeneutic phenomenological study, we used a semi-structured interview guide to collect data from a purposive and snowball sample of 40 people with disabilities and their caregivers. The participants were from 11 districts in 6 provinces in Zambia. The in-depth interviews were done between July 2022 and November 2022. Data were managed in NVivo and analysed using reflexive thematic analysis. The three themes included: (1) awareness and experience of public health measures on COVID-19 among people with disabilities; (2) experience of othering and stigmatisation as people with disability during the COVID-19 pandemic and (3) experience of COVID-19 symptoms and having COVID-19 among people with disabilities. Interventions were largely unresponsive to the needs of people with disabilities, exacerbating the risk of exposure to infection. In future, adaptations like emergency risk communication in braille, audio and sign language interpretation in adapted communication formats should be made. Further studies are needed to quantify the gaps in access to health, explore policies and strategies to improve health outcomes for people with disabilities in LMICs like Zambia. The findings may contribute to the development and enhancement of policies and interventions responsive to the needs of people with disabilities in future pandemics in the Zambian context.

Reverse Sign Language Recognition System Using Machine Learning

Deaf and hard-of-hearing people communicate with each other and their society by using sign language. As this is a very natural method for pupils to stay in touch with computers, many academics are working on it to make it less complex and more convenient for use. So the main objective of gesture recognition research is to make systems which can interact and communicate while understanding human gestures and use them. In simple words to communicate information. Fast and extremely accurate hand detection and realtime hand gestures. Identification should be possible with vision-based hand gesture circumstances and interfaces. Learning and knowing sign movements and gestures is the kick start in making words and sentences for computer assisted sign language interpretation. Both dynamic and static sign actions are open and available. Both ways of gesture recognition are crucial to human culture, even if static gesture recognition is much easier than dynamic gesture recognition. When a human enters the value of alphabets and numerical value as input, the system immediately displays or outputs the appropriate recognised character shows the gesture on the monitor screen. In the following, research projects that have led to a proper system that uses convolutional neural networks to identify handwriting on the basis of the depth pictures and Hand Languages (Brain lipi) the collects. Keywords- Convolutional neural network,Text recognition ,Convert to image,StackGAN,simultaneous Tracking text and converting to image, gesture recognition,display output as sign gesture,training machine to(A-z to 1-0).

Dynamic gesture recognition using hand pose-based neural networks for sign language interpretation

Dynamic gesture recognition using hand pose-based neural networks for sign language interpretation

”Kieli oli tässä kohtaa vain väline” – kulttuuritulkkien, opettajien ja kuraattoreiden näkemyksiä kielestä ja kulttuuritulkkien roolista

Alongside public service interpreting consisting of language interpretation, many institutions, including institutions of learning, increasingly use intercultural mediators to communicate with migrants. Drawing on the research tradition of sociology of translation, we analyze our data from two perspectives, mobilizing the sociological concepts of role, agency, and (affective) facework, as well as the linguistic anthropological concept of language ideology. The data consist of semi-structured thematic interviews with intercultural mediators and teachers and school social workers who work with them. The analysis shows that intercultural mediators’ affective labor, based on their broad agency, is fundamental in building trust between migrant families and the school. Intercultural mediation is different from public service interpreting, where monolithic, referential language ideologies are often felt to prevent the achievement of mutual understanding between the participants.

Examining the association between professional language interpreter services and intensive care unit utilization among patients with non-English language preference: Evidence from a large U.S. hospital

ObjectiveTo examine the healthcare utilization of patients with non-English language preference (NELP) who utilized a professional language interpreter (PLI) in the intensive care unit (ICU) compared to similar patients with NELP who did not utilize a PLI in the ICU. MethodsSingle center cohort study of patients with NELP with at least one ICU admission a large academic medical center in the U.S. Midwest (1/1/2008-12/31/2022). The first model examined ICU length-of-stay (LOS) using a negative binomial and the second model examined whether a patient was readmitted to the ICU using a logistic regression with each model controlling for PLI utilization and covariates. ResultsPatients with NELP who utilized a PLI in the ICU had 0.87-days longer in the ICU (p<0.01) and had a 46% decreased odds of being readmitted to the ICU (p<0.01) than a comparable patient with NELP who did not utilize a PLI in the ICU. ConclusionProviding patients with NELP with access to a PLI in the ICU can improve patient outcomes and reduce language barriers. Practice implicationsThese results can provide the justification to potentially increase PLI staffing levels or increase the access to existing PLIs for more patients with NELP in ICUs.

A wearable sign language translation device utilizing silicone-hydrogel hybrid triboelectric sensor arrays and machine learning

Sign language is a crucial communication tool for the hearing impaired to interact with the outside world. However, the low prevalence of sign language leads to significant communication barriers between the hearing impaired and others. These barriers can be alleviated by using electronic sign language translation devices, but such devices typically face challenges related to their bulkiness, rigidity, and high cost. Herein, we present a cost-effective, flexible, and wearable device designed for the interpretation of sign language, leveraging machine learning algorithms to transform sign language movements into spoken language accurately. Our device is equipped with flexible and stretchable triboelectric sensor (FS-TS) arrays and a printed circuit board for efficient signal processing and wireless transmission. These FS-TSs are constructed from cost-effective materials including silicone, hydrogel, and fluorinated ethylene propylene (FEP) powders, which, while affordable, ensure rapid response and heightened sensitivity. By analyzing 1000 sets of sign language gestures with machine learning, our system has achieved an impressive recognition accuracy of 98.5 %. This achievement underscores the potential of our system as an economically viable and scalable solution for enhancing sign language recognition within the wearable electronics domain.

Innovative Hand Sign to Text-and-Speech Conversion System

In present scenario, it is difficult for people who are unable to speak due to various reasons. It may not be a permanent condition, as muteness and deafness can be caused or manifest due to several different phenomena, such as physiological injury, illness, medical side effects, psychological trauma, developmental disorders, or neurological disorders. People who are suffering from this typically use the Sign Language to communicate with other people. However, it is not possible for everyone to be acquainted with communication using sign language. This project focuses on developing a solution which can be used by regular people to understand the people having mutism. Hand Sign Recognition Systems have evolved significantly with advancements in machine learning and deep learning techniques. These systems are designed to recognize gestures or signs, making them useful in various applications such as sign language interpretation, human-computer interaction, and virtual reality. This paper gives an overview of the current state of hand sign recognition systems, highlighting the techniques used, challenges, and possible future directions. The focus of the paper is on deep learning-based methods and their impact on the accuracy and usability of hand sign recognition in real-time applications [1]

AUTOMATED GESTURE RECOGNITION USING APPLIED LINGUISTICS WITH DATA-DRIVEN DEEP LEARNING FOR ARABIC SPEECH TRANSLATION

Gesture recognition for Arabic speech translation includes developing advanced technologies that correctly translate body and hand movements corresponding to Arabic sign language (ArSL) into spoken Arabic. This leverages machine learning and computer vision techniques in complex systems simulation platforms to scrutinize the gestures utilized in ArSL, detecting mild differences in facial expressions, hand shapes, and movements. Sign Language Recognition (SLR) is paramount in assisting communication for the Deaf and Hard-of-Hearing communities. It includes using vision-based methods and Surface Electromyography (sEMG) signals. The sEMG signal is crucial for recognizing hand gestures and capturing muscular activities in sign language. Researchers have comprehensively shown the capability of EMG signals to approach specific details, mainly in classifying hand gestures. This progression is a stimulating feature in extracting the interpretation and recognition of sign languages and investigating the phonology of signed language. Leveraging machine learning algorithms and signal processing techniques in complex systems simulation platforms, researchers aim to extract relevant traits from the sEMG signals that correspond to different ArSL gestures. This study introduces an Enhanced Dwarf Mongoose Algorithm with a Deep Learning-Driven Arabic Sign Language Detection (EDMODL-ASLD) technique on sEMG data. In the initial phase, the presented EDMODL-ASLD model is subjected to data preprocessing to change the input sEMG data into an attuned format. In the next stage, feature extraction with fractal theories is used to gather relevant and nonredundant data from the EMG window to construct a feature vector. In this study, the absolute envelope (AE), energy (E), root-mean square (RMS), standard deviation (STD), and mean absolute value (MAV) are the five time-domain extracted features for the EMG window observation. Meanwhile, the dilated convolutional long short-term memory (ConvLSTM) technique is used to identify distinct sign languages. To improve the results of the dilated ConvLSTM model, the hyperparameter selection process is executed using the EDMO model. To illustrate the significance of the EDMODL-ASLD technique, a brief experimental validation is made on the Arabic SLR database. The experimental validation of the EDMODL-ASLD technique portrayed a superior accuracy value of 96.47% over recent DL approaches.

The Development of AI-Powered Automatic Video Sign Language Translation Systems

People who are deaf or hard of hearing often face challenges in fully understanding written subtitles in videos due to the differences between sign language and spoken language, each having unique grammar and structures. While many social media platforms provide automatic subtitles, they are often insufficient for accessibility. Sign language interpretation should be included to make video content fully accessible. Traditionally, adding sign language to videos involves a time-consuming process of recording and embedding separate translation videos, which must be redone with any changes to the original video. The plugin simplifies this process by dynamically translating updates directly from subtitle files, significantly reducing the time, effort, and cost involved. It allows seamless sign language support for any video without modifying the original content. The plugin integrates with video players, providing a user-controlled, customizable sign language window that can be activated, moved, resized, or turned off as desired.

Workshops: Enhancing communication for nursing students with patients with hearing disabilities

Abstract Background Nursing students need guidance on effective communication strategies for interacting with patients with hearing disabilities, emphasizing the importance of addressing this gap in their education as future healthcare professionals. Methods A cross-sectional survey was conducted among nursing students at seven institutions before and after workshops led by guides with hearing disabilities from a non-profit organization Ma’agalei Shema and funded by the National Insurance Institute. Data was collected from 2023 to March 2024 and analyzed using SPSS software. Results 279 participants completed the survey before the workshops. and 136 responding afterward. Almost 80% were female. Before the workshops, 68.46% of students expected to gain communication tools for patients with hearing disabilities. About one-third believed the healthcare system offers accessible treatment to these patients, while only 22.22% foresaw no barriers. One-third reported no clinical contact with such patients. Among those who did, lip-reading (25.53%), writing (17.73%), assistance from family members (15.6%), and sign language (9.22%) were commonly used. Only 2.84% used professional sign language interpreters, and 20.57% reported never meeting a patient with hearing disabilities (8% reported “other”). Post-workshop surveys showed that around 82.48% of participants felt the workshops would significantly enhance their communication skills with such patients, with only 5.11% reporting a lack of practical tools received. Conclusions Practical workshops can significantly enhance communication skills and improve patient care. This study underscores the potential impact of minor adjustments in communication strategies on patient care, such as increasing the use of professional interpreters. Main messages: Providing training to nursing students in effective communication with these patients can streamline interactions, diminish reliance on family intermediaries, and enhance medical care. Key messages • Nursing students need guidance on communicating with patients with hearing disabilities to improve future healthcare interactions. • Practical workshops enhance communication skills, stressing the importance of minor adjustments for better patient care.