Background Ethical sensitivity is a prerequisite for ethical nursing practices. Efforts to improve nurses’ ethical sensitivity are required to correctly recognise ethical conflicts and for sound decision-making. Because an emerging infectious disease response involves complex ethical issues, it is important to understand the factors that influence public health nurses’ ethical sensitivity while caring for patients with COVID-19, an emerging infectious disease. Objectives This study aims to identify the relationship between nursing professionalism, the organisation’s ethical climate, and the ethical sensitivity of nurses who care for emerging infectious disease patients in Korean public health centres. Further, it sought to identify factors influencing ethical sensitivity and the mediating effect of the organisational ethical climate to inform guidelines and improve ethical sensitivity. Research design This was a cross-sectional descriptive study. Participants and research context Data were collected from February 3 to 8 March 2021. Participants included 167 nurses caring for patients with COVID-19 in public health centres in South Korea. Ethical consideration This study was approved by the Institutional Review Board of the Chung-Ang University and followed the principles of research ethics. Results The factors influencing ethical sensitivity were working at a COVID-19 disease direct response department, nursing professionalism, and organisation’s ethical climate. The organisation’s ethical climate showed a partial mediating effect on the influence of nursing professionalism on ethical sensitivity. Conclusion Our findings show that nurses’ ethical sensitivity can be improved by refining the organisation’s ethical climate and nursing professionalism.

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transistors, [1] displays, [2] solar cells, [3] and biosensors. [4] In particular, meniscus-guided coating (MGC) such as zone casting, [5] slot die coating, [6] and solution shearing [7] is especially promising due to their compatibility to industrially relevant "sheet2sheet" and "roll2roll" processes. [8] For solution shearing, solution is sandwiched between a heated substrate and a blade, [9] where the solution forms a curved air-liquid interface (i.e., meniscus); as the blade moves, thinfilm is generated across the substrate due to solidification (i.e., a process involving supersaturation of the solution, followed by nucleation and growth) occurring near the meniscus. [10] Here, the thin-film properties (e.g., crystal alignment, crystal size) are strongly dependent on the solidification process, which in turn determines the performance of the thin-film device. Therefore, it is of critical significance to understand how the experimental parameters affect the solidification process so that it can be precisely controlled.The determining factors that affect solidification process are the fluid dynamic phenomena occurring near the meniscus, [10,11] such as capillary flow, Marangoni flow, viscous drag, and solvent evaporation. [12] Each of these effects is intricately dependent on multiple solution shearing parameters (e.g., shearing speed, substrate temperature), is complexly intertwined to affect theThe ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/adma.202105035.

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Self-supervised learning (SSL) approaches such as wav2vec 2.0 and HuBERT models have shown promising results in various downstream tasks in the speech community. In particular, speech representations learned by SSL models have been shown to be effective for encoding various speech-related characteristics. In this context, we propose a novel automatic pronunciation assessment method based on SSL models. First, the proposed method fine-tunes the pre-trained SSL models with connectionist temporal classification to adapt the English pronunciation of English-as-a-second-language (ESL) learners in a data environment. Then, the layer-wise contextual representations are extracted from all across the transformer layers of the SSL models. Finally, the automatic pronunciation score is estimated using bidirectional long short-term memory with the layer-wise contextual representations and the corresponding text. We show that the proposed SSL model-based methods outperform the baselines, in terms of the Pearson correlation coefficient, on datasets of Korean ESL learner children and Speechocean762. Furthermore, we analyze how different representations of transformer layers in the SSL model affect the performance of the pronunciation assessment task.