Ergonomic Risk-prone Activities toward Nurses in the Intensive Care and Emergency Room

BACKGROUND: Nurses have the risk of ergonomic hazards in providing nursing care, especially with increasingly dynamic health services such as during Coronavirus disease-19 pandemic like today. AIM: The aim of the study was to evaluate activities prone to produce ergonomic risks during the implementation of nursing care in intensive care and emergency room (ER) of a hospital in Riau, Indonesia. METHODOLOGY: This study was conducted by observing the routine activities conducted by the nurses and using similar task group techniques equipped with Rapid Entire Body Assessment instrument. Those observed activities were obtained from 17 intensive care room nurses and ten ER nurses. There were six activities observed in the intensive care room: Bathing, transferring the patient, wounds dressing, taking blood samples for the AGDA examinations, as well as inserting the intravenous needle and electrocardiograms. Meanwhile, there were two activities observed in the ER: Transferring the patient and inserting the intravenous needle. RESULTS: The highest ergonomic risks activity in the intensive care room was bathing the patient with a total score of 13. At the ER, the highest risk score was transferring the patient with a total score of 12. Both activities were at level 4, indicating a high-risk condition. Thus, examinations and changes should be immediately initiated. CONCLUSION: The results are significant to be paid attention by the related parties at the hospital to facilitate some improvements immediately. In addition, the ergonomic approaches that can be suggested to the nurses are regular stretching, physical exercises, and applying ergonomic principles while working.

When Preschoolers Interact with an Educational Robot, Does Robot Feedback Influence Engagement?

In this paper, we examine to what degree children of 3–4 years old engage with a task and with a social robot during a second-language tutoring lesson. We specifically investigated whether children’s task engagement and robot engagement were influenced by three different feedback types by the robot: adult-like feedback, peer-like feedback and no feedback. Additionally, we investigated the relation between children’s eye gaze fixations and their task engagement and robot engagement. Fifty-eight Dutch children participated in an English counting task with a social robot and physical blocks. We found that, overall, children in the three conditions showed similar task engagement and robot engagement; however, within each condition, they showed large individual differences. Additionally, regression analyses revealed that there is a relation between children’s eye-gaze direction and engagement. Our findings showed that although eye gaze plays a significant role in measuring engagement and can be used to model children’s task engagement and robot engagement, it does not account for the full concept and engagement still comprises more than just eye gaze.

Vocal markers of Autism Spectrum Disorder: assessing the generalizability of machine learning models

Background: Machine learning (ML) approaches show increasing promise to identify vocal markers of Autism Spectrum Disorder (ASD). Nonetheless, it is unclear to what extent such markers generalize to new speech samples collected in diverse settings such as using a different speech task or a different language. Aim: In this paper, we systematically assess the generalizability of ML findings across a variety of contexts. Methods: We re-train a promising published ML model of vocal markers of ASD on novel cross-linguistic datasets following a rigorous pipeline to minimize overfitting, including cross-validated training and ensemble models. We test the generalizability of the models by testing them on i) different participants from the same study, performing the same task; ii) the same participants, performing a different (but similar) task; iii) a different study with participants speaking a different language, performing the same type of task. Results: While model performance is similar to previously published findings when trained and tested on data from the same study (out-of-sample performance), there is considerable variance between studies. Crucially, the models do not generalize well to new similar tasks and not at all to new languages. The ML pipeline is openly shared. Conclusion: Generalizability of ML models of vocal markers - and more generally biobehavioral markers - of ASD is an issue. We outline three recommendations researchers could take in order to be more explicit about generalizability and improve it in future studies.

User Satisfaction Reward Estimation Across Domains: Domain-independent Dialogue Policy Learning

Learning suitable and well-performing dialogue behaviour in statistical spoken dialogue systems has been in the focus of research for many years. While most work that is based on reinforcement learning employs an objective measure like task success for modelling the reward signal, we propose to use a reward signal based on user satisfaction. We propose a novel estimator and show that it outperforms all previous estimators while learning temporal dependencies implicitly. We show in simulated experiments that a live user satisfaction estimation model may be applied resulting in higher estimated satisfaction whilst achieving similar success rates. Moreover, we show that a satisfaction estimation model trained on one domain may be applied in many other domains that cover a similar task. We verify our findings by employing the model to one of the domains for learning a policy from real users and compare its performance to policies using user satisfaction and task success acquired directly from the users as reward.

Synchronous web-based collaborative writing: Attitudes of learners toward working in pairs and small groups

This study explored learners’ attitudes toward collaborative writing in pairs and small groups (fours and sixes) in a synchronous web-based environment. Sophomore pre-service teachers in one intact class in the Philippines (n=31) completed the same collaborative writing tasks using Google Docs. In three separate out-of-class sessions, they first worked in pairs, and then they were assigned to either groups of four or groups of six in the succeeding two sessions. After completing the tasks, they were asked to complete a post-task questionnaire. The learners had an overall positive attitude toward peer collaborative writing in a web-based synchronous environment as it helps them to develop the content better, find appropriate vocabulary, and improve the grammatical and mechanical accuracy of the texts they produced. Students highly appreciated working in pairs and groups of four. In pairs, they felt that it is easier to manage text-chat deliberation, resolve concerns, and attend to each other’s suggestions. In groups of four, they acknowledged the increase of peer resources for knowledge sharing and in ensuring the accuracy of their language use. Hence, the majority complained that a group of six is not that conducive in a real-time text-chat environment. When asked about their preference, most students would prefer to work in a similar task and environment in pairs. These findings on learners’ attitudes toward collaborative writing concur with the previous literature in face-to-face educational settings and open new insights on synchronous web-based collaborative writing via text-chat.

Diversity promotes abstraction and cognitive flexibility in collective problem solving

Social interaction plays an important role in many contexts of human reasoning and problem solving, and groups are often found to outperform individuals. We suggest that this benefit is associated with the dialogical sharing and integration of diverse perspectives and strategies. Here, we investigated whether diversity in prior experience affects groups’ problem representations and performance. In a game-like experiment, participants categorized aliens based on combinations of their features. Whenever a specific feature combination was learned, the rule changed and a new feature combination had to be learned. However, unbeknown to participants, rule changes were governed by an abstract meta-rule and awareness of this provided an advantage when rules changed. We compared categorization performance between individuals, groups composed of members trained on the same rule, and groups composed of members trained on different rules before entering the collaborative test phase. Following preregistered predictions, groups with diverse task experience outperformed groups with similar task experience, which in turn outperformed individuals. These findings were unaffected diversity in personality (Big Five) and motivational factors, suggesting that diversity in experience plays the key role. We conclude that cognitive diversity impact problem solving by stimulating processes of abstraction and flexibility at the level of the group.

The Perceptions of Students Learning Turkish as a Foreign Language Towards "Writing in Turkish"

This study aimed to determine the perceptions of students who learn Turkish as a foreign language towards "writing in Turkish." The study was conducted using the phenomenology pattern, one of the qualitative research methods. The study sample consisted of one hundred seventy-five (175) students who were from two state universities in Turkey and learned Turkish as a foreign language in the 2019-2020 academic year. The study data were collected using an online form, and the participant students were asked to complete the statement in the form as follows: "Writing in Turkish is like ……, because ……………". As a result of the research, the students generated one hundred and eleven (111) valid metaphors about "writing in Turkish." Ninety (90) of them were positive, and 21 were negative. The categories with the highest number of positive responses were as follows: "Writing in Turkish: an Enjoyable Task" (n: 20), "Writing in Turkish: an Improving Task" (n: 17), "Writing in Turkish: a Similar Task" (n: 13) and "Writing in Turkish: an Achievable Task" (n: 12). The category with the most negative responses was the "Writing in Turkish: a Difficult Task" (n: 12).

Heritage languages and the ʻmultilingual boostʼ: intercomprehension skills of Russian and Polish heritage speakers in Germany

The article focuses on whether and to which extent heritage bilinguals make use of their heritage language while developing receptive skills in unknown languages which are either related to the majority language or the heritage language. Thirty four adolescent heritage speakers of Russian and Polish and a control group of thirty three German monolinguals were first exposed to a text in Swedish. The monolingual control group was matched with regard to age, educational background, foreign languages learned at school as well as proficiency in English. All participants had to determine the parts of speech of ten items from the text, translate them into German, and extract the main pieces of information from the text. In a second step, the heritage speakers completed a similar task with an unknown Slavic target language (Serbian). The results revealed no bilingual advantage of the heritage speakers over the monolinguals in the Swedish task. Furthermore, they scored lower in the Serbian trial. We treat this as evidence that access to the heritage language as a resource for solving these tasks is limited compared to the majority language and English which might be due to lesser metalinguistic knowledge about structures of the heritage language.

Generalization of attentional control strategies across distinct tasks

Does attentional control strategy generalize across different visual search tasks? Previous research has failed to observe significant correlations in strategy metrics between different visual search tasks (Clarke et al., 2020), suggesting that strategy is not unitary, or determined by a single trait variable. Here we question just how heterogeneous (non-unitary) strategies are, hypothesizing a similarity gradient account, which holds that strategy does generalize to some degree, specifically across tasks with similar attentional components. To test this account, we employed the Adaptive Choice Visual Search (ACVS; Irons & Leber, 2018a), a visual search paradigm designed to directly measure attentional control strategy. In two studies, we had participants complete the ACVS and a modified, but similar, task with one altered attentional component (specifically, the requirement to use feature-based attention and enumeration, respectively). We found positive correlations in strategy optimality between tasks that do vs. do not involve feature-based attention (r = .38, p = .0068) and across tasks that do vs. do not require enumeration (r = .33, p = .018). Thus, attentional control strategies did generalize across sufficiently similar tasks, although the strength of the correlations was weaker than the within-task test-retest reliability of strategy measure. These results support the similarity gradient account.