Physical and biomimetic treatment methods to reduce microplastic waste accumulation.

Background: Since the Covid-19 pandemic in 2019, the use of plastics has increased exponentially, so it is imperative to manage and dispose of these plastic wastes safely. Objectives: This review focuses on the management strategies governed by the policies of each country to reduce plastic waste through physical collection methods and methods that use eco-imitation technologies. Results: Thus far, physical treatment methods have been applied to sewage and drinking water treatment. The abilities of bio-inspired treatment methods are being assessed in terms of capturing microplastics (MPs) and nanoplastics (NPs), extracting substances from marine organisms, reducing toxicity, and developing alternatives to petroleum-based plastics. Conclusions: Various post-treatment methods have been proposed to collect and remove MPs and NPs that have reached into aquatic ecosystems and subsequently reduce their toxicity. However, there are limitations that the effectiveness of these methods is hindered by the lack of policies governing the entire process of plastic use before the post-treatment. Purpose of Review: We purpose to reduce plastic waste through methods that use eco-imitation technologies. Recent Findings: These eco-imitation methods are attracting attention as viable future plastic waste treatment options in line with the goals of sustainable development.

An Analysis of the Effects of Psychosocial Work Environment on the Retention Intentions of Primary Care Coordinators Nursing Patients with Chronic Illness.

This study aimed to investigate the effects of psychosocial work environment on the retention intentions of care coordinators taking care of patients with chronic illness. A descriptive survey study was conducted with a convenience sample of care coordinators who organized patients and treatment teams that offered professional and persistent treatment. A total of 132 participants were recruited from 19 October to 19 November 2020. The data were analyzed through descriptive statistics, t-tests, ANOVA, Scheffé post hoc, and hierarchical multiple regression using SPSS 26.0. The results showed that work organization and job content (ß = 0.254, p = 0.014) and value at the workplace (ß = 0.245, p = 0.034) had significant effects on the retention intentions of participants. The final model of the study explained 40.1% of participants' retention intentions (F = 11.830, p < 0.001). The development of educational programs and implementation of policies for improving the psychosocial work environment were found to be essential for increasing the retention intentions of professional care coordinators.

Can social media data be used to evaluate the risk of human interactions during the COVID-19 pandemic?

The U.S. has taken multiple measures to contain the spread of COVID-19, including the implementation of lockdown orders and social distancing practices. Evaluating social distancing is critical since it reflects the risk of close human interactions. While questionnaire surveys or mobility data-based systems have provided valuable insights, social media data can contribute as an additional instrument to help monitor the risk of human interactions during the pandemic. For this reason, this study introduced a social media-based approach that quantifies the pro/anti-lockdown ratio as an indicator of the risk of human interactions. With the aid of natural language processing and machine learning techniques, this study classified the lockdown-related tweets and quantified the pro/anti-lockdown ratio for each state over time. The anti-lockdown ratio showed a moderate and negative correlation with the state-level social distancing index on a weekly basis, suggesting that people are more likely to travel out of the state where the higher anti-lockdown level is observed. The study further showed that the perception expressed on social media could reflect people's behaviors. The findings of the study are of significance for government agencies to assess the risk of close human interactions and to evaluate their policy effectiveness in the context of social distancing and lockdown.