Acne care in health care providers during the COVID-19 pandemic: A national survey.

The medical face mask, widely used by health care providers (HCPs) during the COVID-19 pandemic, is reported to be associated with adverse reactions, among which acne is one of the most common. This study aims to evaluate treatment strategies employed by HCPs affected by acne in association with prolonged medical face mask use, their openness towards accessing telemedicine as a patient, and other lifestyle factors with potential influence on the evolution of their acne. Our online-based cross-sectional survey was distributed between December 17, 2020, and February 17, 2021, and targeted HCPs from different medical centers in Romania. From the n = 134 respondents, 50% reported current acne lesions and 56.7% required treatment. Of the latter, 65.8% self-medicated and 34.2% sought medical advice. The most common treatment associations between anti-acne topical products were: retinoids and salicylic acid (18.18%; n = 8), retinoids and benzoyl peroxide (13.64%; n = 6), salicylic acid and benzoyl peroxide (13.64%; n = 6), and azelaic acid together with salicylic acid (9.09%; n = 4). The health care provider responders were reluctant to use telemedicine, as only 14.2% participants were open to telemedicine. Our results suggest inadequate management of acne in HCPs using medical face masks. As with other occupational hazards and proper usage of personal protective equipment, HCPs should receive adequate screening, training, and treatment for this condition.

Identifying the Structure of CSCL Conversations Using String Kernels

Computer-Supported Collaborative Learning tools are exhibiting an increased popularity in education, as they allow multiple participants to easily communicate, share knowledge, solve problems collaboratively, or seek advice. Nevertheless, multi-participant conversation logs are often hard to follow by teachers due to the mixture of multiple and many times concurrent discussion threads, with different interaction patterns between participants. Automated guidance can be provided with the help of Natural Language Processing techniques that target the identification of topic mixtures and of semantic links between utterances in order to adequately observe the debate and continuation of ideas. This paper introduces a method for discovering such semantic links embedded within chat conversations using string kernels, word embeddings, and neural networks. Our approach was validated on two datasets and obtained state-of-the-art results on both. Trained on a relatively small set of conversations, our models relying on string kernels are very effective for detecting such semantic links with a matching accuracy larger than 50% and represent a better alternative to complex deep neural networks, frequently employed in various Natural Language Processing tasks where large datasets are available.

Before and during COVID-19: A Cohesion Network Analysis of students' online participation in moodle courses.

The COVID-19 pandemic has changed the entire world, while the impact and usage of online learning environments has greatly increased. This paper presents a new version of the ReaderBench framework, grounded in Cohesion Network Analysis, which can be used to evaluate the online activity of students as a plug-in feature to Moodle. A Recurrent Neural Network with LSTM cells that combines global features, including participation and initiation indices, with a time series analysis on timeframes is used to predict student grades, while multiple sociograms are generated to observe interaction patterns. Students' behaviors and interactions are compared before and during COVID-19 using two consecutive yearly instances of an undergraduate course in Algorithm Design, conducted in Romanian using Moodle. The COVID-19 outbreak generated an off-balance, a drastic increase in participation, followed by a decrease towards the end of the semester, compared to the academic year 2018-2019 when lower fluctuations in participation were observed. The prediction model for the 2018-2019 academic year obtained an R 2 of 0.27, while the model for the second year obtained a better R 2 of 0.34, a value arguably attributable to an increased volume of online activity. Moreover, the best model from the first academic year is partially generalizable to the second year, but explains a considerably lower variance (R 2 = 0.13). In addition to the quantitative analysis, a qualitative analysis of changes in student behaviors using comparative sociograms further supported conclusions that there were drastic changes in student behaviors observed as a function of the COVID-19 pandemic.