Stress reactions and health problems in hospital personnel during the COVID-19 pandemic-A longitudinal, multicenter study

Background: The overarching aim of the study was to (1) investigate how working with COVID-19 patients has impacted work environment, and (2) to identify how factors in the work environment impact adverse health outcomes among hospital personnel (HP), throughout the four waves of the pandemic. Material(s) and Method(s): In a web-based survey altogether 2472 HP participated from four large university hospitals in Norway, whereof N = 680 in April-June 2020 (T1), N = 1073 in December-January 2020/2021 (T2), N = 818 in April-May 2021 (T3), and N = 972 in December 2021-February 2022 (T4). At each time point participants reported on pandemic related work tasks, work environment and adverse health outcomes. Somatic symptoms, psychological distress, posttraumatic stress symptoms and burnout served as outcomes of multivariable linear regression models. The percentage of responders involved in ICU treatment of COVID-19 patients varied between 21% and 40% from T1-T4. Result(s): Reported stressors altered in strength during the 4 waves. Preliminary results indicate that exposure to patients with COVID-19 was associated with more frequent experience of work environmental factors. Compared to colleagues not working with patients with COVID-19 HP reported challenges related to professional competency and training, predictability in teams and work environment, manageable workload, adequate help and support for work stress management, user-friendliness of Personal Protection Equipment and infection protection safety. Furthermore, these environmental factors were associated with symptoms of psychological unhealth on at least one timepoint. Conclusion(s): The results may help guide organizational efforts to maintain professional competency and to reduce stress more efficiently among hospital personnel at different stages in response to long-term crises.

Predicting Student's Satisfaction towards Hybrid Learning in Informatics

This paper used regression and moderation approaches to evaluate the student's satisfaction with informatics towards the hybrid learning in their study. Multiple Linear Regression (MLR) identified student satisfaction based on hybrid learning difficulty and benefit ($p < 0.001$). Linear Regression (LR) found hybrid learning benefits impacted the student's satis-faction significantly $(p < 0.001$). Student's $t$-test also revealed that Overall Satisfaction (OS) significantly affected hybrid learning's satisfaction ($p < 0.001$). Analysis of Co-variants (ANCOVA) also proved that hybrid learning's benefit ($p < 0.001$) and OS ($p < 0.05$) significantly influenced student satisfaction. The paper also proved that hybrid learning's benefits positively correlate with student satisfaction (0.596). The slopes of 'Yes' and 'No' are substantially different from one another when the probability value of 0.22 $(p > 0.05$). Hence, no moderator (OS) affects the relationship's strength between the benefit and satisfaction of hybrid learning. The paper also revealed that hybrid learning's difficulty has a negative correlation (-.18), and the benefit of hybrid learning is positively associated with student satisfaction (.66). Implementing a hybrid learning mode during Covid-19 periods significantly impacted student satisfaction and the decision taken by the administration was also meaningful. © 2023 IEEE.

Using the "omnicell" automatic drug dispensing cabinet to monitor sedative drugs in a critical care unit

Introduction: Automatic drug dispensers are now widely used in critical care.1-2 They can provide information about dispensed drugs. Good practice in sedation restricts the use of sedatives and titrates doses to defined responses.3-4 Objectives: To extract drug dispenser issuing records for sedatives and link these to patient records to evaluate sedative use. Method(s): in October 2019, we introduced two Omnicell XT automated dispensing cabinets (Omnicell inc. CA, USA) into a 42 bedded general/neurological unit. ICNARC (Intensive care national audit and research centre) and CCMDS (Critical care minimum data set) data was collected using the Ward Watcher program. Dispenser issuing records for alfentanil, propofol and midazolam were obtained as Excel files for 13 months from January 2020. Output time stamps were converted to dates and times. Outputs were linked to outputs of the ICNARC and CCMDS records for the patients that the drugs were issued to. All the outputs had patients identified by their unique hospital numbers. These were used in Excel "power queries" to produce a spread sheet with a single row per patient. Multiple admissions used the first diagnosis, the final outcome and the total length of stay. The total dose of sedatives was calculated from ampoule dose and number. The duration of treatment was calculated from the first and last issues of the drug. ICNARC codes were used to identify the primary system in the admission diagnostic code and those patients admitted for COVID-19. Variables were compared using Chi Squared, Mann-Whitney U and Kruskal Wallis Tests. The significance of associations was established using Spearman's Rho. Linear regression was used to define relationships more clearly. Result(s): Table one summarises the patient characteristics with respect to all admissions during the study period and for patients who had had the studied drugs issued to them. Midazolam was used in fewer patients, they were more likely to be male, heavier (p>0001) and to die than patients receiving Propofol or Alfentanil (p>0.001). With respect to diagnostic groups, all the sedatives, particularly Midazolam (p<0.001), were more likely to be used in patients with COVID-19. The relationship between the dose of sedative drugs and patient age and weight was explored using the dose per advanced respiratory day. All three drugs had a significant but weak negative relationship with age, lower doses being given to older people (Propofol r2 = 0.02, p=0.01. Alfentanil r2 = 0.04, p=0.00. Midazolam r2 = 0.07, p=0.00.). There was also a weak but significant relationship between increasing dose of Propofol with patient weight (r2 = 0.02, p=0.01), but there was no relation between weight and doses of the other drugs. Conclusion(s): Information from automatic drug dispensers can be interpreted and combined with other datasets to produce clinically relevant information. The limited weak relationships between drug dose and age and weight suggests that sedative drugs could have been better titrated to response.

Smart Tourism using Attractive and Safe Travel Recommendation Technology

The tourism sector was among the most affected sector during the COVID-19 pandemic and has lost up to USD 5.87 billion potential revenue. Since many countries closed the borders, including Indonesia, by applying travel restrictions and thus tourists postponed their visits. Whereas vaccine distribution has shown good progress as the vaccination percentage in Jakarta and Bali has shown promising results since the majority of its population has been vaccinated, and it helps many industries, including tourism, recover. However, the pandemic might change tourist behavior. In addition, information about tourist destinations is spread poorly in various sources, and it psychologically affects tourists' decision to visit. Many works have been published to address this issue with the recommendation system. However, it does not provide geopolitical variables such as PPKM in Indonesia to ensure safeness for the tourist. Therefore, this research aims to enhance innovations in the tourism industry by considering the geopolitics factor into the system using Multiple Linear Regression. The result of this research demonstrates the effectiveness of geopolitics added variable on three different cities Jakarta, Java, and Bali. It can be implemented in a wide area in Indonesia. For further research, the proposed model can be used in a wide area in Indonesia and developed for a more comprehensive recommendation system. © 2022 IEEE.

Neighborhood-Level Particle Pollution Assessment during the COVID-19 Pandemic via a Novel IoT Solution

In recent years, the concentration levels of various air pollutants have been constantly increasing, primarily due to the high vehicle flow. In 2020, however, severe lockdowns in Greece were imposed to reduce the spread of the COVID-19 pandemic, which led to a rapid reduction in the concentration levels of air pollutants such as PM2.5 and PM10 in the atmosphere. Initially, this paper seeks to identify the correlation between the concentration levels of PM10 and the traffic flow by acquiring data from low-cost IoT devices which were placed in Thessaloniki, Greece, from March to August 2020. The correlation and the linearity between the two parameters were further investigated by applying descriptive analytics, regression techniques, Pearson correlation, and independent T-testing. The obtained results indicate that the concentration levels of PM10 are strongly correlated to the vehicle flow. Therefore, the results hint that the decrease in the vehicle flow could result in improving the quality of environmental air. Finally, the acquired results point out that the temperature and humidity are weakly correlated with the concentration levels of PM10 present in the atmosphere.

Stacking Classifier-based Automated Insurance Fraud Detection System

During the Covid-19 pandemic, the insurance industry's digital shift quickened, resulting in a surge in insurance fraud. To combat insurance fraud, a system that securely manages and monitors insurance processes must be built by combining a machine learning classification framework with a web application. Examining and identifying fraudulent features is a frequent method of detecting fraud, but it takes a long time and can result in false results. One of these issues is addressed by the proposed solution. By digitalizing the paper-based workflow in insurance firms, this paper intends to improve the efficiency of the existing approach. This method also aimed to improve the present approach's data management by integrating a web application with a machine learning stacking classifier framework experimented on a linear regression-based iterative imputed data for detecting fraud claims and making the entire claim processing and documentation process more robust and agile. © 2022 IEEE.

Assessment of changes in air quality in Bangalore, India, during the Covid-19 pandemic lockdown: Statistical modelling

COVID-19 pandemic has resulted in a halt to the daily lifestyle of people around the world and bound them to abide by the lockdown measures enforced to prevent the disease from further spreading. In India also, lockdown has been enforced from March 2020. As a result, the level of air pollutants in the atmosphere goes on decreasing. To know the air quality pattern of Bangalore city, ten stations around the city were selected. Air quality data of these stations has been availed from the Central Pollution Control Board (CPCB) of India website. Box chart concept of graphical representation has been applied to show the range of temporal variation of the air pollutants selected (CO, NO2, Ozone, PM2.5, PM10 and SO2) for the study area over two distinct periods (pre-lockdown and post-lockdown). It has been observed that all the pollutants level were drastically or significantly reduced except for SO2 which showed mixed behavior during the entire study period probably due to no restriction on the operation of power plants. GIS based contour mapping is done for each pollutant over the entire study area and separately for two distinct periods (pre-lockdown and post-lockdown). It was found that, change in CO level over the entire study area was significant and the reason behind it was complete restriction on vehicular movement which is the primary reason for CO emission in atmosphere. Reduction in PMs and ozone was also noticeable, but change in SO2 over the entire study area was almost insignificant. To find out the probable sources of pollution during the lockdown and before the lockdown period and the most significant parameters statistical approach has been adopted. The whole data set has been grouped based on similarity and divided into three distinct clusters for both pre-lockdown and post-lockdown period separately using Hierarchical Agglomerative Cluster Analysis (HACA) concept. Principal Component Analysis (PCA) was done for each of the clusters and each time period considered. From the results of PCA it can be confirmed that the most significant parameters were PM10, PM2.5, ozone and SO2. Results suggest that the probable sources of pollution during pre-lockdown period were vehicular emissions, power plants, industrial activities etc. In contrast, during post-lockdown period the sources of pollution were power plants, construction sites and household pollution only. MLR (Multiple Linear Regression) models were developed to predict Air Quality Index (AQI). Most of the models showed good fit with adjusted R2 value more than 0.9. Regression coefficient (R2) values for PM10 followed PM2.5 were highest in each cluster. © 2023 Author(s).

Sociopsychological Factors Related to Information Seeking during the COVID-19 Pandemic

Objectives: To limit the risk of contracting the coronavirus, it is crucial for individuals to attain accurate COVID-19 related information. Once they are equipped with such information, they can engage in risk preventive behaviors. This study aimed to examine the sociopsychological factors predisposing individuals' information-seeking intentions. Method(s): Based on the risk information seeking and processing (RISP) model, we hypothesized that individuals perceiving the risk of COVID-19 were likely to seek risk-related information through increased affective response (i.e., anxiety and fear) and information insufficiency. We considered perceived information gathering capacity as a moderator in the prediction of information-seeking intention. Using an online survey platform, study participants were recruited from US adults. Multivariate linear regressions were conducted in a hierarchical fashion while controlling for numerous covariates. Result(s): A total of 510 responses were included in our analyses. Respondents' mean age was 46.6+/-17.8 years;about half (53.3%) were females. Results showed that respondents' perceived risk provoked affective responses (regression coefficient (b) = 0.8417, 95% CI [0.7408, 0.9426]), which then increased self-perceived information insufficiency (b = 0.1857, 95% CI [0.0859, 0.2855]). This finding indicated that after perceiving the risk of coronavirus, individuals experienced worry and fear associated with the risk. Such affective responses made them realize that their current COVID-19 related knowledge was insufficient. Also, respondents who acknowledged information insufficiency were motivated to seek information about the coronavirus (b = 0.1099, 95% CI [0.0198, 0.1999]). The relation between information insufficiency and information-seeking intentions was moderated by perceived information gathering capacity (b = 0.0070, 95% CI [0.0001, 0.0151]), indicating that individuals with a higher capacity of gathering information were more likely to intend information seeking. Conclusion(s): Study findings suggest the importance of interventions to promote information seeking for individuals with a low information gathering capacity. Policy makers and clinicians assist the public in obtaining accurate information from reliable sources.Copyright © 2023

Patient Survey in Medical Practices on their Visit during the SARS-CoV-2 Pandemic.

Background: Since the beginning of the SARS-CoV2 pandemic medical practices have implemented diverse protective measures to contain the pandemic, practice organization and structures were adapted. In order to get information about patients' perception of their doctors' visit during the pandemic, we conducted a patient survey in medical practices. Method(s): Cross-sectional study of 58 patients, who were interviewed in four medical practices (family physicians and specialists) in the South West of Munich from 02.04.-17.04.2020 on the following topics: "personal risk assessment", "sense of security and perception of protection measures in the practice setting", "importance of the doctor's visit" and "change of medication, nicotine consumption". By means of a questionnaire with 24 items, data were collected anonymously. Results are presented descriptively and via ANOVA as well as via linear regression. Result(s): The personal risk assessments for COVID-19-disease and for a severe course of COVID-19 were rated low moderate, independent of sex or age. Around 8% of the surveyed patients discussed their personal risk with their doctors. The sense of security in the practice setting was rated high. The rating of the protection equipment was good as well, and closely met expectations. The personal importance of the visit varied. Only 6% had considered cancelling their visit beforehand. A change of medication due to SARS-CoV-2 pandemic was not observed. Conclusion(s): The patient survey provides a snapshot of the outpatient setting from the patient's perspective in a hyperdynamic pandemic situation. Yet, due to the small study population, the results have to be interpreted with caution.Copyright © 2021, Deutscher Arzteverlag.

Comparative Analysis of Linear Regression and Logistic Regression for the COVID-19 Detection from CT-Scans

The new emerging Coronavirus disease (COVID-19) is a pandemic disease due to its enormous infectious capability. Generally affecting the lungs, COVID-19 engenders fever, dry cough, and tiredness. However, some patients may not show symptoms. An imaging test, such as a chest X-ray or a chest CT scan, is therefore requested for reliable detection of this pneumonia type. Despite the decreasing trends both in the new and death reported cases, there is an extent need for quick, accurate, and inexpensive new methods for diagnosis. In this framework, we propose two machine learning (ML) algorithms: linear regression and logistic regression for effective COVID-19 detection in the abdominal Computed Tomography (CT) dataset. The ML methods proposed in this paper, effectively classify the data into COVID-19 and normal classes without recourse to image preprocessing or analysis. The effectiveness of these algorithms was shown through the use of the performance measures: accuracy, precision, recall, and F1-score. The best classification accuracy was obtained as 96% with logistic regression using the saga solver with no added penalty against 95.3% with linear regression. As for precision, recall, and F1-score the value of 0.89 was reached by logistic regression for all these metrics, as well as the value of 0.87 by linear regression. © 2022 IEEE.

The effect of eating behaviour upon BMI in women with gestational diabetes

Aims: Gestational diabetes has been attributed to maternal obesity and suboptimal maternal diet but the relative contribution of maternal eating behaviour is unknown. We compared eating behaviour in women with gestational diabetes and non-pregnant adults, and assessed which eating behaviour traits were most strongly associated with BMI in women with gestational diabetes. Method(s): Participants (total n = 448) including men (n = 67), non-pregnant women (n = 181) and women with gestational diabetes during a singleton pregnancy (n = 200;29 weeks' gestation;NICE / Covid-19 criteria) were recruited prospectively and completed a three-factor eating questionnaire (TFEQ-R18). Associations between BMI and uncontrolled eating (UE), emotional eating (EE) and cognitive restraint (CR) were assessed using linear regression. Result(s): Women with gestational diabetes had significantly lower UE scores compared to men (53% vs 63%;p < 0.001) and non-pregnant women (53% vs 65%;p < 0.001), and lower EE scores compared to non-pregnant women (60% vs 70%;p < 0.001). In men, BMI showed positive associations with UE (Coeff 25.2;95% CI 10.8-39.6;p = 0.001) and EE scores (Coeff 11.9;95% CI 3.3-20.6;p = 0.007) while CR had no significant association. In non-pregnant women, BMI showed positive associations with UE (Coeff 20.7 95% CI 11.4-30.0), p < 0.001) and EE scores (Coeff 7.7;95% CI 1.8-13.6;p = 0.010) and negative associations with CR (Coef-10.6;95% CI -21.1 to -0.1;p = 0.049). In women with gestational diabetes, only EE scores were significantly associated with BMI (Coeff 7.8;95% CI 3.9-11.7;p < 0.001). Conclusion(s): Women with gestational diabetes have favourable eating behaviour compared to men and non-pregnant women. Addressing EE may provide new translational opportunities to reduce BMI in gestational diabetes.

Detecting COVID-19 spreading phases with time series analysis

Piecewise linear regression (PLR) method is applied to study cumulative cases of COVID-19 evolving everyday in England up to 6th February 2022 just before travel restrictions are removed and people started not to get tested anymore in the UK and factors e.g. the lockdowns behind the spread COVID-19 are also investigated. It is clear that different periods exhibit distinct patterns depending on variants and government-imposed restriction. Therefore, the effectiveness of lockdown measures is evaluated by comparing the rate of increase after a certain period (delay effect of measures) and that of time before as well as how new variants take over as a dominant variant. In addition, autoregression function is studied to show strong effect of cases in the past on today's cases since the disease is highly infectious. Most of work is carried out thorough python built-in libraries such as pandas for preprocessing data and matplotlib which allows us to gain more insight and better visualization into the real scenario. Visualization is conducted by Geoda showing the regional level of infections. © 2023 SPIE.

Relative Effectiveness of Vaccination and Non-Pharmaceutical Interventions on COVID-19 Infection and Deaths in the US

Objectives: To examine the relative effectiveness of vaccination and non-pharmaceutical interventions (NPIs) on COVID-19 infection, reproduction rate, and deaths in the US. Method(s): Retrospective national-level US data were obtained from the Oxford COVID-19 Government Response Tracker (OxCGRT dataset). We performed time-trend analyses from December 2020 to December 2022 to observe how the values of policy variables and the number of COVID-19 new cases and deaths changed over time. The policy variables included (1) the number of people fully vaccinated per 100 of the total population (referred to as vaccination rate) and (2) the stringency index (a proxy for NPIs since it is a composite measure based on nine response indicators including school closures, workplace closures, stay-at-home requirements, and travel bans). We also performed multivariable linear regression to examine the associations between the policy variables and the COVID-19 reproduction rate. Result(s): Based on the time-trend analyses, the number of people vaccinated started to rise since March 2021, while the stringency index had steadily declined since early January 2021. A decrease in new COVID-19 cases and deaths was also observed during these three months (January to March 2021). However, despite a higher vaccination rate than in early 2021, new COVID-19 cases and deaths peaked in late 2021 and early 2022, suggesting that some NPIs might still be needed. The multivariable linear regression analysis showed that the reproduction rate of COVID-19 was negatively associated with the stringency index (coefficient = -0.010, 95% CI -0.013 to -0.005) and vaccination rate (coefficient = -0.005, 95% CI -0.009 to -0.001), after controlling for time covariates. Conclusion(s): The study highlighted the importance of NPIs in reducing new COVID-19 cases and deaths even when vaccination was in progress. Further research accounting for other factors is needed to disentangle the effects of NPIs and other measures from vaccination campaigns.Copyright © 2023

Analysis and Prediction of Student Performance Based on Moodle Log Data using Machine Learning Techniques

During the COVID-19 Pandemic, many universities in Thailand were mostly locked down and classrooms were also transformed into a fully online format. It was challenging for teachers to manage online learning and especially to track student behavior since the teacher could not observe and notify students. To alleviate this problem, one solution that has become increasingly important is the prediction of student performance based on their log data. This study, therefore, aims to analyze student behavior data by applying Predictive Analytics through Moodle Log for approximately 54,803 events. Six Machine Learning Classifiers (Neural Network, Random Forest, Decision Tree, Logistic Regression, Linear Regression, and Support Vector Machine) were applied to predict student performance. Further, we attained a comparison of the effectiveness of early prediction for four stages at 25%, 50%, 75%, and 100% of the course. The prediction models could guide future studies, motivate self-preparation and reduce dropout rates. In the experiment, the model with 5-fold cross-validation was evaluated. Results indicated that the Decision Tree performed best at 81.10% upon course completion. Meanwhile, the SVM had the best result at 86.90% at the first stage, at 25% of the course, and Linear Regression performed with the best efficiency at the middle stages at 70.80%, and 80.20% respectively. The results could be applied to other courses and on a larger e-learning systems log that has similar student activity conditions and this could contribute to more accurate student performance prediction © 2023, International Journal of Emerging Technologies in Learning.All Rights Reserved.

Generalized Linear Regression Model to Determine the Threshold Effects of Climate Variables on Dengue Fever: A Case Study on Bangladesh

One of the leading causes of the increase in the intensity of dengue fever transmission is thought to be climate change. Examining panel data from January 2000 to December 2021, this study discovered the nonlinear relationship between climate variables and dengue fever cases in Bangladesh. To determine this relationship, in this study, the monthly total rainfall in different years has been divided into two thresholds: (90 to 360 mm) and (<90 or >360 mm), and the daily average temperature in different months of the different years has been divided into four thresholds: (16degreeC to <=20degreeC), (>20degreeC to <=25degreeC), (>25degreeC to <=28degreeC), and (>28degreeC to <=30degreeC). Then, quasi-Poisson and zero-inflated Poisson regression models were applied to assess the relationship. This study found a positive correlation between temperature and dengue incidence and furthermore discovered that, among those four average temperature thresholds, the total number of dengue cases is maximum if the average temperature falls into the threshold (>28degreeC to <=30degreeC) and minimum if the average temperature falls into the threshold (16degreeC to <=20degreeC). This study also discovered that between the two thresholds of monthly total rainfall, the risk of a dengue fever outbreak is approximately two times higher when the monthly total rainfall falls into the thresholds (90 mm to 360 mm) compared to the other threshold. This study concluded that dengue fever incidence rates would be significantly more affected by climate change in regions with warmer temperatures. The number of dengue cases rises rapidly when the temperature rises in the context of moderate to low rainfall. This study highlights the significance of establishing potential temperature and rainfall thresholds for using risk prediction and public health programs to prevent and control dengue fever.Copyright © 2023 Shamima Hossain.

Impact of Provider Nudges on Herpes Zoster Vaccine Series Completion during the COVID-19 Pandemic in the United States

Objectives: To determine the impact of a pharmacy-based, clinical decision support (CDS) tool on herpes zoster (HZ) vaccine series completion during the initial months of the COVID-19 pandemic across the US. Method(s): In partnership with Kroger Health, a pharmacy CDS tool alerted staff of patients due for their second HZ vaccine dose, which had been accompanied previously by a timed text message. Once operations changed due to COVID-19, the system limited outreach to only patients visiting the pharmacy. Primary outcomes included the proportion of patients receiving both doses within a Kroger-owned pharmacy (n=2,293) and the number of days between doses, both within and across two 32-week periods before and after the pandemic hit the US. Generalized estimating equation-based (GEE) logistic and linear regression models determined differences in completion rates and time to completion. Result(s): During the observation period, 38,937 adults received at least one HZ vaccine dose, with 77.2% receiving both doses. Patients engaged by the CDS tool achieved 80.5% dose completion, versus 65.4% of those not intervened (p<0.0001), which was lower than in the period immediately before the pandemic (85.8%, p<0.0001). The dosing window averaged 119.4 days (SD: 26.91), which was the longest timeframe between doses since the HZ vaccine was launched and nearly one month longer than before the pandemic (93.0 days [SD: 28.02], p<0.0001). The odds of dose completion increased in areas of higher health literacy (OR: 1.01;95% CI: 1.007-1.014), but decreased in areas of higher poverty (OR: 0.992;95% CI: 0.988-0.995). Time to completion was slightly shorter (B=-0.04, p<0.05) in areas of higher health literacy. Conclusion(s): Despite changes in clinical processes, a nationwide community pharmacy was successful in completing HZ vaccine dose series for adults during the pandemic, suggesting that processes in community pharmacies can protect staff while remaining committed to providing preventive health services during public health crises.Copyright © 2023

Characterizing the Blood Pressure Response to Physical Counterpressure Manoeuvres Using Surface Electromyography in Adults With Long Covid

Orthostatic intolerance (OI) is common in Long Covid. Physical counterpressure manoeuvres (PCM) may improve OI in other disorders. We characterised the blood pressure-rising effect of PCM using surface electromyography (sEMG) and investigated its association with fatigue in adults with Long Covid. Participants performed an active stand with beat-to-beat hemodynamic monitoring and sEMG of both thighs, including PCM at 3-minutes post-stand. Multivariable linear regression investigated the association between change in systolic blood pressure (SBP) and change in normalised root mean square (RMS) of sEMG amplitude, controlling for confounders including the Chalder Fatigue Scale (CFQ). In 90 participants (mean age 46), mean SBP rise with PCM was 13.7 (SD 9.0) mmHg. In regression, SBP change was significantly, directly associated with change in RMS sEMG ( 0.25 , 95% CI 0.07-0.43, P = 0.007);however, CFQ was not significant. PCM measured by sEMG augmented SBP without the influence of fatigue. Copyright © 2013 IEEE.

Labour Productivity Analysis of Manufacturing Sector in Turkey against Eu

This study offers an in-depth analysis of labour productivity of manufacturing sector in Turkey and provides a comparison with EU27 and EA19 countries utilizing Eurostat time series data of 63 quarters covering 2005/first quarter-2020/third quarter time interval. Productivity trends are identified and interpreted by relating them with the key macroeconomic events and factors. Multiple linear and non-linear regression equations, and ARIMA model with different parameters are applied to the time series data considering the periods with and without covid effect. Future projections are made for the periods 2020-2023 for Turkey manufacturing sector based on the best fitting regression and ARIMA solutions and they are compared. Findings revealed that extreme covid conditions of even two quarters of data have significant impact on the forecasted values for Turkey, EU27 and EA19 countries. ARIMA analysis with 12 different parameter settings provided accurate results, supported by Thiel's inequality coefficients and standard error measures. Analysis has shown consistent patterns between EA19 and EU27 countries. ARIMA results represent better compatibility with the regression results for Turkey. Study is valuable by providing comprehensive and comparative analysis, revealing future forecasts and covid effect and degree of recovery from the pandemic.

Maternal Mental Health during the COVID-19 Pandemic and Its Impact on Child Cognitive Development

Introduction: The COVID-19 pandemic has had a significant impact on pregnant persons' mental health. Prepandemic data reports an impact of depression, anxiety, and stress on the emotional and behavioral development of the child. Objective(s): We aimed to evaluate the impact of gestational maternal depression, anxiety, and stress during the COVID-19 pandemic on the child's cognitive development at 18 months. Method(s): The CONCEPTION study is a prospective mother-child cohort, established since June 23, 2020, during the COVID-19 pandemic. Depression and anxiety were assessed during pregnancy using validated tools in French and English (Edinburgh Postnatal Depression Scale [EPDS] and General Anxiety Disorder-7 [GAD-7]) as well as stress and antidepressant consumption. The child's cognitive development was reported by the mother using the third edition of Ages and stages questionnaires (ASQ-3) at 18 months of age. Data on other covariates were collected electronically. Multivariate linear regression models were built to assess the association between prenatal maternal depression, anxiety, stress, and child development across domains: communication, gross motor, fine motor, problem solving, and personal-social domains while adjusting for covariates. In addition, sensitivity analyses have been added like COVID-19 diagnosis. Result(s): Overall, 445 mother-child dyads were included in analyses (mean gestational age at delivery 39.2 weeks +/-1.8). Mean gestational scores were, for depression (EDPS, 7.8+/-5.4), anxiety (GAD-7, 4.4+/-4.0), and stress (4.3+/-2.1). Adjusting for potential confounders, as well as for maternal depression and anxiety during pregnancy, maternal prenatal stress was associated with communication skills (adjusted beta = 1.5, CI 95 % (0.34, 2.7)) and fine motor skills (adjusted beta = 1.06, CI 95 % (0.02, 2.6)) at 18 months age. Gestational depression, anxiety, and antidepressants use were not associated with any of the ASQ-3's domains. In addition, no significant association was found in stratified analysis for COVID-19 diagnosis. Conclusion(s): During the COVID-19 pandemic, gestational maternal stress was associated with some aspects of childhood cognitive problems, including communication and fine motor skills. Our results highlight the need to continue following-up on children until kindergarten to better understand the impact of maternal mental health during pregnancy on the child's cognitive development in the era of COVID-19.

Predicting Success: An Examination of the Predictive Validity of a Measure of Motivational-Developmental Dimensions in College Admissions.

Amid the COVID-19 pandemic, an unprecedented number of higher education institutions adopted test-optional admissions policies. The proliferation of these policies and the criticism of standardized admissions tests as unreliable predictors of applicants' postsecondary educational promise have prompted the reimagining of evaluative methodologies in college admissions. However, few institutions have designed and implemented new measures of applicants' potential for success, rather opting to redistribute the weight given to other variables such as high school course grades and high school GPA. We use multiple regression to investigate the predictive validity of a measure of non-cognitive, motivational-developmental dimensions implemented as part of a test-optional admissions policy at a large urban research university in the United States. The measure, composed of four short-answer essay questions, was developed based on the social-cognitive motivational and developmental-constructivist perspectives. Our findings suggest that scores derived from the measure make a statistically significant but small contribution to the prediction of undergraduate GPA and 4-year bachelor's degree completion. We also find that the measure does not make a statistically significant nor practical contribution to the prediction of 5-year graduation.