Visualising the Optimistic, Realistic, and Pessimistic Financial Distress Outlooks for Airport Operations in Malaysia

This paper aims to visualise three financial distress outlooks using computer simulations. The financial distress exposure for airport operations in Malaysia between 1991 and 2021 is given by Altman Z”-score and modelled by the multivariate generalized linear model (MGLM). Seven determinants contributing to the financial distress from literature are examined. The determinant series are fitted individually by using linear model with time series components and autoregressive integrated moving average models to forecast values for the next 10 financial years. Future short- to long-term memory effects following COVID-19 are apparent in time series plots. In the simulations, the MGLM procedure utilised Gaussian, gamma, and Cauchy probability distributions associated with expectations and challenges of doing business as well as uncertainties in the economy. The underlying trends of realistic, optimistic, and pessimistic financial distress outlooks insinuate that the increasing risk of financial distress of airport operations in Malaysia is expected to continue for the next decade.

Association between children's home-schooling and parental psychological distress during the COVID-19 pandemic in Taiwan: Risk and protective factors in a multilevel approach.

BACKGROUND: The role home-schooling of children in parental mental health during the COVID-19 pandemic in Taiwan remains unknown. This study aimed to assess the association between parental psychological distress and home-schooling in a socio-ecological context during the peak of the first wave of the COVID-19 pandemic in Taiwan. METHODS: This was a prospective cohort study. In total, 902 parents (father: n = 206, mother: n = 696) who home-schooled children under 18 years of age were recruited by purposive sampling from 17 cities in Taiwan. Data were collected between 19 July and 30 September 2021 through a survey. Multilevel regression models were used to examine the association between parents' psychological distress and home-schooling considering the characteristics at the person and city levels. RESULTS: Parental psychological distress was positively associated with difficulty in setting up electronic devices and increased disputes between parents and children, and it was negatively associated with time management and increased time spent bonding with their children during home-schooling (Ps < 0.05). Parents who had a child with health conditions, lived in an extended family, worked from home, lived during the Level 3 alert level, and lived with a median/sporadic level of the COVID-19 community spread by city also reported greater psychological distress (Ps < 0.05). However, parents who had greater household family support reported less psychological distress (P < .05). CONCLUSIONS: Clinicians and policy makers must carefully consider parental mental health while home-schooling during the COVID-19 pandemic in a broader socio-ecological context. A focus is advised on the home-schooling experiences of parents and other risk and protective factors for parental psychological distress at the person and city levels, especially for those with children who require medical interventions and have a medical condition.

Robust smoothing of left-censored time series data with a dynamic linear model to infer SARS-CoV-2 RNA concentrations in wastewater

Wastewater sampling for the detection and monitoring of SARS-CoV-2 has been developed and applied at an unprecedented pace, however uncertainty remains when interpreting the measured viral RNA signals and their spatiotemporal variation. The proliferation of measurements that are below a quantifiable threshold, usually during non-endemic periods, poses a further challenge to interpretation and time-series analysis of the data. Inspired by research in the use of a custom Kalman smoother model to estimate the true level of SARS-CoV-2 RNA concentrations in wastewater, we propose an alternative left-censored dynamic linear model. Cross-validation of both models alongside a simple moving average, using data from 286 sewage treatment works across England, allows for a comprehensive validation of the proposed approach. The presented dynamic linear model is more parsimonious, has a faster computational time and is represented by a more flexible modelling framework than the equivalent Kalman smoother. Furthermore we show how the use of wastewater data, transformed by such models, correlates more closely with regional case rate positivity as published by the Office for National Statistics (ONS) Coronavirus (COVID-19) Infection Survey. The modelled output is more robust and is therefore capable of better complementing traditional surveillance than untransformed data or a simple moving average, providing additional confidence and utility for public health decision making. © 2023, American Institute of Mathematical Sciences. All rights reserved.

Investigating the impact of CO2emissions on the COVID-19 pandemic by generalized linear mixed model approach with inverse Gaussian and gamma distributions

Carbon dioxide (CO2) rate within the atmosphere has been rising for decades due to human activities especially due to usage of fuel types such as coal, cement, flaring, gas, oil, etc. Especially in 2020, COVID-19 pandemic caused major economic, production, and energy crises all around the world. As a result of this situation, there was a sharp decrease in the global CO2 emissions depending on the fuel types used during this pandemic. The aim of this study was to explore the effects of "CO2 emissions due to the fuel types"on "percentage of deaths in total cases"attributed to the COVID-19 pandemic using generalized linear model and generalized linear mixed model (GLMM) approaches with inverse Gaussian and gamma distributions, and also to obtain global statistical inferences about 169 World Health Organization member countries that will disclose the impact of the CO2 emissions due to the fuel types during this pandemic. The response variable is taken as "percentage of deaths in total cases attributed to the COVID-19 pandemic"calculated as "(total deaths/total confirmed cases attributed to the COVID-19 pandemic until December 31, 2020)∗100."The explanatory variables are taken as "production-based emissions of CO2 from different fuel types,"measured in tonnes per person, which are "coal, cement, flaring, gas, and oil."As a result of this study, according to the goodness-of-fit test statistics, "GLMM approach with gamma distribution"called "gamma mixed regression model"is determined as the most appropriate statistical model for investigating the impact of CO2 emissions on the COVID-19 pandemic. As the main findings of this study, 1 t CO2 emissions belonging to the fuel types "cement, coal, flaring, gas, and oil"per person cause increase in deaths in total cases attributed to the COVID-19 pandemic by 2.8919, 2.6151, 2.5116, 2.5774, and 2.5640%, respectively. © 2023 the author(s), published by De Gruyter.

Road crashes in Adelaide metropolitan region, the consequences of COVID-19.

Background: Many countries instituted lockdown rules as the COVID-19 pandemic progressed, however, the effects of COVID-19 on transportation safety vary widely across countries and regions. In several situations, it has been shown that although the COVID-19 closure has decreased average traffic flow, it has also led to an increase in speeding, which will indeed increase the severity of crashes and the number of fatalities and serious injuries. Methods: At the local level, Generalized linear Mixed (GLM) modelling is used to look at how often road crashes changed in the Adelaide metropolitan area before and after the COVID-19 pandemic. The Geographically Weighted Generalized Linear Model (GWGLM) is also used to explore how the association between the number of crashes and the factors that explain them varies across census blocks. Using both no-spatial and spatial models, the effects of urban structure elements like land use mix, road network design, distance to CBD, and proximity to public transit on the frequency of crashes at the local level were studied. Results: This research showed that lockdown orders led to a mild reduction (approximately 7%) in crash frequency. However, this decrease, which has occurred mostly during the first three months of the lockdown, has not systematically alleviated traffic safety risks in the Greater Adelaide Metropolitan Area. Crash hotspots shifted from areas adjacent to workplaces and education centres to green spaces and city fringes, while crash incidence periods switched from weekdays to weekends and winter to summer. Implications: The outcomes of this research provided insights into the impact of shifting driving behaviour on safety during disorderly catastrophes such as COVID-19.

Intensity and lag-time of non-pharmaceutical interventions on COVID-19 dynamics in German hospitals.

Introduction: Evaluating the potential effects of non-pharmaceutical interventions on COVID-19 dynamics is challenging and controversially discussed in the literature. The reasons are manifold, and some of them are as follows. First, interventions are strongly correlated, making a specific contribution difficult to disentangle; second, time trends (including SARS-CoV-2 variants, vaccination coverage and seasonality) influence the potential effects; third, interventions influence the different populations and dynamics with a time delay. Methods: In this article, we apply a distributed lag linear model on COVID-19 data from Germany from January 2020 to June 2022 to study intensity and lag time effects on the number of hospital patients and the number of prevalent intensive care patients diagnosed with polymerase chain reaction tests. We further discuss how the findings depend on the complexity of accounting for the seasonal trends. Results and discussion: Our findings show that the first reducing effect of non-pharmaceutical interventions on the number of prevalent intensive care patients before vaccination can be expected not before a time lag of 5 days; the main effect is after a time lag of 10-15 days. In general, we denote that the number of hospital and prevalent intensive care patients decrease with an increase in the overall non-pharmaceutical interventions intensity with a time lag of 9 and 10 days. Finally, we emphasize a clear interpretation of the findings noting that a causal conclusion is challenging due to the lack of a suitable experimental study design.

Asynchronous Changepoint Estimation for Spatially Correlated Functional Time Series.

We propose a new solution under the Bayesian framework to simultaneously estimate mean-based asynchronous changepoints in spatially correlated functional time series. Unlike previous methods that assume a shared changepoint at all spatial locations or ignore spatial correlation, our method treats changepoints as a spatial process. This allows our model to respect spatial heterogeneity and exploit spatial correlations to improve estimation. Our method is derived from the ubiquitous cumulative sum (CUSUM) statistic that dominates changepoint detection in functional time series. However, instead of directly searching for the maximum of the CUSUM-based processes, we build spatially correlated two-piece linear models with appropriate variance structure to locate all changepoints at once. The proposed linear model approach increases the robustness of our method to variability in the CUSUM process, which, combined with our spatial correlation model, improves changepoint estimation near the edges. We demonstrate through extensive simulation studies that our method outperforms existing functional changepoint estimators in terms of both estimation accuracy and uncertainty quantification, under either weak or strong spatial correlation, and weak or strong change signals. Finally, we demonstrate our method using a temperature data set and a coronavirus disease 2019 (COVID-19) study. Supplementary materials accompanying this paper appear online. Supplementary materials for this article are available at 10.1007/s13253-022-00519-w.

An extended health belief model for COVID-19: understanding the media-based processes leading to social distancing and panic buying.

Building on the health belief model (HBM), this research tests, over six months, how the exposure to COVID-related information in the media affects fear, which in turn conditions beliefs about the severity of the virus, susceptibility of getting the virus, and benefits of safety measures. These health beliefs ultimately lead to social distancing and panic buying. As a first contribution, we find that fear is not directly triggered by the objective severity of a crisis, but rather formed over time by the way individuals are exposed to media. Second, we show that fear affects behaviors through the components of the HBM which relate to the risks/benefits of a situation. Last, we find that critical thinking about media content amplifies the "adaptive" responses of our model (e.g., health beliefs, social distancing) and reduces its "maladaptive" responses (e.g., panic buying). Interestingly, we note that the beneficial effect of critical thinking about media content disappears as the level of fear increases over time. The implications of these findings for policymakers, media companies, and theory are further discussed. Supplementary Information: The online version contains supplementary material available at 10.1007/s11747-022-00865-8.

The primary drivers of private-sphere pro-environmental behaviour in five European countries during the Covid-19 pandemic.

Nowadays, when fighting climate change and other global environmental issues is of utmost urgency, the understanding of what drives pro-environmental behaviour has become a hot topic in both academic and practical circles. Some earlier studies unravelled the complexity of the underlying factors of pro-environmental conduct, yet more studies are needed to understand the socio-cultural premises of such behaviour in different countries. The main aim of this paper is to offer a comprehensive analysis of the importance of pro-environmental behaviour drivers across five countries in Europe, namely Greece, Poland, Portugal, Sweden, and the United Kingdom. The survey using computer-assisted web interviews (N = 2502) was implemented in July 2020, the first summer of the Covid-19 pandemic. Hierarchical linear models were employed to analyse individuals' behaviour, defined as self-perceived declaration of the willingness to contribute to environmental conservation. The results showed that attitudinal and value-related factors are more significant than demographics. Biospheric values and relationship to nature generally affect pro-environmental behaviour positively. Similarly, the experience of Covid-19 exerted a positive influence. In terms of country-level predictors, greenhouse gas emissions were found to have affected pro-environmental behaviour negatively, while the share of renewable energy sources influenced it positively. The cumulative country Covid-19-related mortality at the time of investigation did not have discernible impact. Based on the results it is suggested that, in order to foster PEB, a stronger emphasis on environmental education and attitudes towards nature should be employed.

Variation of Air Quality in Four Districts of Lima in the Period of the COVID-19 Pandemic

The purpose of the study was to evaluate the variation of air quality parameters: PM10, PM2.5, NO2, and O3 in four districts of Lima-Peru (Carabayllo, San Juan de Lurigancho, Villa María del Triunfo, and Jesús María) in the period 2015-2019 and 2020-2021. Likewise, the ozone variability in the Carabayllo district was modelled. Pollutant concentration data were collected from the National Service of Meteorology and Hydrology of Peru (SENAMHI) from the 4 stations located in the aforementioned districts. The data was processed with the IBM SPSS Statistics v.25 software. A statistically significant decrease was observed between the 2015-2019 and 2020-2021 periods in pollutants PM10, PM2.5, and NO2, in the four monitoring stations, mainly because the country entered in a state of emergency (quarantine due to COVID -19). However, an increase in O3 was observed, attributed to the decrease in NOX concentrations. Finally, the gamma generalized linear model represented 87.6% of the ozone variability in the Carabayllo district, showing a good fit for the field data. © 2022 IEEE.

Predictors of Decision Regret among Caregivers of Older Canadians Receiving Home Care: A Cross-Sectional Online Survey.

Background. In Canada, caregivers of older adults receiving home care face difficult decisions that may lead to decision regret. We assessed difficult decisions and decision regret among caregivers of older adults receiving home care services and factors associated with decision regret. Methods. From March 13 to 30, 2020, at the outbreak of the COVID-19 pandemic, we conducted an online survey with caregivers of older adults receiving home care in the 10 Canadian provinces. We distributed a self-administered questionnaire through Canada's largest and most representative private online panel. We identified types of difficult health-related decisions faced in the past year and their frequency and evaluated decision regret using the Decision Regret Scale (DRS), scored from 0 to 100. We performed descriptive statistics as well as bivariable and multivariable linear regression to identify factors predicting decision regret. Results. Among 932 participants, the mean age was 42.2 y (SD = 15.6 y), and 58.4% were male. The most frequently reported difficult decisions were regarding housing and safety (75.1%). The mean DRS score was 28.8/100 (SD = 8.6). Factors associated with less decision regret included higher caregiver age, involvement of other family members in the decision-making process, wanting to receive information about the options, and considering organizations interested in the decision topic and health care professionals as trustworthy sources of information (all P < 0.001). Factors associated with more decision regret included mismatch between the caregiver's preferred option and the decision made, the involvement of spouses in the decision-making process, higher decisional conflict, and higher burden of care (all P < 0.001). Discussion. Decisions about housing and safety were the difficult decisions most frequently encountered by caregivers of older adults in this survey. Our results will inform future decision support interventions. Highlights: This is one of the first studies to assess decision regret among caregivers of older adults receiving home and community care services and to identify their most frequent difficult decisions.Difficult decisions were most frequently about housing and safety. Most caregivers of older adults in all 10 provinces of Canada experienced decision regret.Factors associated with less decision regret included higher caregiver age, the involvement of other family members in the decision-making process, wanting to receive information about the options, considering organizations interested in the decision topic, and health care professionals as trustworthy sources of information. Factors associated with more decision regret included mismatch between the caregiver's preferred option and the decision made, the involvement of spouses in the decision-making process, higher decisional conflict, and higher burden of care.

Modeling the evolution of deaths from infectious diseases with functional data models: The case of COVID-19 in Brazil.

In this paper, we apply statistical methods for functional data to explore the heterogeneity in the registered number of deaths of COVID-19, over time. The cumulative daily number of deaths in regions across Brazil is treated as continuous curves (functional data). The first stage of the analysis applies clustering methods for functional data to identify and describe potential heterogeneity in the curves and their functional derivatives. The estimated clusters are labeled with different "levels of alert" to identify cities in a possible critical situation. In the second stage of the analysis, we apply a functional quantile regression model for the death curves to explore the associations with functional rates of vaccination and stringency and also with several scalar geographical, socioeconomic and demographic covariates. The proposed model gave a better curve fit at different levels of the cumulative number of deaths when compared to a functional regression model based on ordinary least squares. Our results add to the understanding of the development of COVID-19 death counts.

Factors Associated with Mortality in Patients Hospitalized with COVID-19 Using Generalized Linear Model (Ordinal Logistic Regression) in North of Iran

Background and purpose: Controlling the severity and death of the COVID-19 disease is still a major challenge. This research aimed at identifying the factors associated with mortality in hospitalized patients with COVID-19 applying generalized linear model. Materials and methods: In this cross-sectional study, demographic and clinical data of COVID-19 patients hospitalized with positive RT-PCR test results (n=6759) in Mazandaran hospitals (August 2019) were obtained from the national registration system for COVID-19. SPSS V27 and R V4.0 were used for data analyses and multivariate generalized linear model test with an ordinal logistic scale was applied. Results: Findings showed that full recovery and relative recovery occurred in 5888 (87.11%) and 400 (5.92%) patients, respectively. Mortality rate was 6.97%. The chance of death in patients with relative recovery (49.55%) compared with those with full recovery (21.61%) was almost 2.3 times higher. Predictors of mortality in these patients included age over 60 years (OR: 1.60), ICU admission (OR: 5.09), intubation (OR: 4.10), SpO2≤ 93% (OR: 2.41), cancer (OR: 1.74), diabetes (OR: 1.29), heart disease (OR: 1.41), and chronic kidney disease (OR: 2.17). Conclusion: Specific and timely medical care, considering the mentioned risk factors, should be introduced and provided to prevent mortality in patients hospitalized with COVID-19. © 2022, Mazandaran University of Medical Sciences. All rights reserved.

Impact of Selected Meteorological Factors on COVID-19 Incidence in Southern Finland during 2020-2021.

We modelled the impact of selected meteorological factors on the daily number of new cases of the coronavirus disease 2019 (COVID-19) at the Hospital District of Helsinki and Uusimaa in southern Finland from August 2020 until May 2021. We applied a DLNM (distributed lag non-linear model) with and without various environmental and non-environmental confounding factors. The relationship between the daily mean temperature or absolute humidity and COVID-19 morbidity shows a non-linear dependency, with increased incidence of COVID-19 at low temperatures between 0 to -10 °C or at low absolute humidity (AH) values below 6 g/m3. However, the outcomes need to be interpreted with caution, because the associations found may be valid only for the study period in 2020-2021. Longer study periods are needed to investigate whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a seasonal pattern similar such as influenza and other viral respiratory infections. The influence of other non-environmental factors such as various mitigation measures are important to consider in future studies. Knowledge about associations between meteorological factors and COVID-19 can be useful information for policy makers and the education and health sector to predict and prepare for epidemic waves in the coming winters.

Discussion of “Multivariate Dynamic Modeling for Bayesian Forecasting of Business Revenue”

This paper, "Multivariate Dynamic Modeling for Bayesian Forecasting of Business Revenue," proposes a novel Bayesian approach based on dynamic linear models to share information from different sectors, LSG (Local Store Group), and item category, through the use of auxiliary information (the discount information). The authors demonstrate the feasibility of parallel computing with multiple item categories, making the Bayesian method highly scalable. The proposed method in the paper should have wide applicability in inventory and revenue management. We suggest in this discussion potential areas for further development.

Forecasting Model Based on Lifestyle Risk and Health Factors to Predict COVID-19 Severity.

The COVID-19 pandemic has now spread worldwide, becoming a real global health emergency. The main goal of this work is to present a framework for studying the impact of COVID-19 on Italian territory during the first year of the pandemic. Our study was based on different kinds of health features and lifestyle risk factors and exploited the capabilities of machine learning techniques. Furthermore, we verified through our model how these factors influenced the severity of the pandemics. Using publicly available datasets provided by the Italian Civil Protection, Italian Ministry of Health and Italian National Statistical Institute, we cross-validated the regression performance of a Random Forest model over 21 Italian regions. The robustness of the predictions was assessed by comparison with two other state-of-the-art regression tools. Our results showed that the proposed models reached a good agreement with data. We found that the features strongly associated with the severity of COVID-19 in Italy are the people aged over 65 flu vaccinated (24.6%) together with individual lifestyle behaviors. These findings could shed more light on the clinical and physiological aspects of the disease.

Forest Environment Association Analysis for the Pandemic Health with Rectified Linear Unit Correlations

The covariance is a measure of the joint variability of two random variables forming a Cartesian coordinate. Variance is a special case of covariance, when two variables are the same. The root of variance is the standard deviation. The normalization of covariance to standard deviation is called Pearson correlation coefficient. The covariance for the region of first and third quadrant is called upper semi-covariance. The covariance for the region of second and fourth quadrant is called down semi-covariance. Here we present semi-covariance, an accurate ReLU (Rectified Linear Unit) way of measuring the non-linear correlation between variables. Our framework is applied to successfully analyze the association between alternative environment social life factors and the pandemic toll recovery health. The results of our analyses of the 2021 pandemic toll suggest that pesticide residual, annual precipitation, forest coverage, economy development, lifestyle, etc. have different impacts on toll of each country. The pesticide may kill immune system, pandemic lifestyle is impacted. We picked total 8 related factors from 22 countries in order to set up future model for digital twin that predicting pandemic trend. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

CellDrift: inferring perturbation responses in temporally sampled single-cell data.

Cells and tissues respond to perturbations in multiple ways that can be sensitively reflected in the alterations of gene expression. Current approaches to finding and quantifying the effects of perturbations on cell-level responses over time disregard the temporal consistency of identifiable gene programs. To leverage the occurrence of these patterns for perturbation analyses, we developed CellDrift (https://github.com/KANG-BIOINFO/CellDrift), a generalized linear model-based functional data analysis method that is capable of identifying covarying temporal patterns of various cell types in response to perturbations. As compared to several other approaches, CellDrift demonstrated superior performance in the identification of temporally varied perturbation patterns and the ability to impute missing time points. We applied CellDrift to multiple longitudinal datasets, including COVID-19 disease progression and gastrointestinal tract development, and demonstrated its ability to identify specific gene programs associated with sequential biological processes, trajectories and outcomes.

The Temporal Lagged Relationship Between Meteorological Factors and Scrub Typhus With the Distributed Lag Non-linear Model in Rural Southwest China.

Background: Meteorological factors can affect the emergence of scrub typhus for a period lasting days to weeks after their occurrence. Furthermore, the relationship between meteorological factors and scrub typhus is complicated because of lagged and non-linear patterns. Investigating the lagged correlation patterns between meteorological variables and scrub typhus may promote an understanding of this association and be beneficial for preventing disease outbreaks. Methods: We extracted data on scrub typhus cases in rural areas of Panzhihua in Southwest China every week from 2008 to 2017 from the China Information System for Disease Control and Prevention. The distributed lag non-linear model (DLNM) was used to study the temporal lagged correlation between weekly meteorological factors and weekly scrub typhus. Results: There were obvious lagged associations between some weather factors (rainfall, relative humidity, and air temperature) and scrub typhus with the same overall effect trend, an inverse-U shape; moreover, different meteorological factors had different significant delayed contributions compared with reference values in many cases. In addition, at the same lag time, the relative risk increased with the increase of exposure level for all weather variables when presenting a positive association. Conclusions: The results found that different meteorological factors have different patterns and magnitudes for the lagged correlation between weather factors and scrub typhus. The lag shape and association for meteorological information is applicable for developing an early warning system for scrub typhus.

Effect of occupational stress on work-life balance and psychological well-being: An empirical study of hyderabad metro rail travelers concerning e-commerce industry employees during COVID-19 pandemic

The researchers present the findings of an empirical study on occupational stress, and its effect on work-life balance and psychological well-being of the E-Commerce Industry employees around Hyderabad, who are using Metro Rail transport to reach the workplace and back home. The study was carried out when the Covid-19 pandemic second wave is at its peak. The Metro Rail journey provides a platform for each commuter to learn new habits continuously, like newspaper reading, improving communication skills, etc. It is also evident from the survey that they build the relationship on a day-to-day basis during their journey time, which helps them to form diversified networking that enables them to understand and empathize with others and the situations. The data was gathered using a research instrument, a structured undisguised questionnaire with 37 statements representing three components of the study - occupational stress, work-life balance, and psychological well-being. The occupational stress and work-life balance were measured using a five-point Likert-type scale. The responses on psychological well-being were gathered using a scale developed by Ryff and Keyes 1995, a seven-point scale. The data gathered this seven-point scale transformed to a five-point scale using the linear transformation method for ease of data analysis. A multivariate Generalized Linear Model which allows more than one dependent variable was used for data analysis. The study was subjected to find whether the E-Commerce industry employees who use the metro rail services to reach the workplace and back home experience what level of stress - low, moderate, or high levels, and its influence on their work-life balance and psychological well-being. Psychological well-being is further measured on six sub-scales, environmental mastery, autonomy, self-acceptance, personal growth, positive relations and purpose of life. The overall Cronbach alpha statistic value for all the study variables is 0.86, work-life balance 0.68, psychological well-being 0.84, and occupational stress 0.62 indicating the research instrument maintained its internal consistency and reliability. Statistically significant differences were observed on occupational stress, age and gender influencing the metro commuter's work-life balance, and some psychological well-being factors. © 2022 International Journal of Health Sciences.All rights reserved.