Impact of Personality Traits and Socio-Demographic Factors on Depression among Doctors and Nurses

The workplace is not immune to conflict and stress, specifically when fulfilling people's responsibilities at great personal costs. Doctors and nurses are always on the frontline in hospitals, vaulting from one stressful high-stakes situation to the next. The HEXACO model of personality traits: Honesty-humility, Emotionality, Extraversion, Agreeableness, Conscientiousness, and Openness, has long been hypothesized to be a major predicting factor when determining individuals' responses to stress and susceptibility to experiencing depression. Most research suggests that personality traits resonate with a person's cognitive abilities and how they can deal with stress and depression. However, there is a lack of research on their correlation to depression severity in the Middle East. The current study aimed to investigate the impact of HEXACO personality traits and socio-demographic factors on depression amongst doctors and nurses. A sample of 170 doctors and nurses (62.1% doctors) completed HEXACO-60 and PHQ-9 depression severity questionnaires. The data were analyzed through descriptive statistics, independent samples t-test, ANOVA, correlation, and regression analysis. The findings showed that Honesty-humility was the strongest predictor, while extraversion was the second strongest. Emotionality had the least impact on depression. The relationship between Agreeableness and Openness to Experience with depression was insignificant. However, gender, age, working hours, and work experience were significant predictors of depression. Marital status and level of specialization were insignificant predictors. Thus, it was concluded that not all HEXACO traits and socio-demographics predict depression. Study findings could be utilized in the implementation of employee recruitment, job crafting, positive psychology, and coaching.

Correlates of COVID-19 vaccination drop-out among health care workers in a tertiary Care hospital of West Bengal

The Government of India launched “COVID-19 vaccination drive” on 16th January, 2021 and health care workers were the first to be prioritised for vaccination. However, the uncertainty regarding safety and efficacy of the vaccine was the major concern amongst them. These led to vaccine hesitancy and ultimately drop out.To estimate the proportion of drop out of COVID-19 vaccination among vaccine-hesitant health care workers (HCWs) of a tertiary care hospital and to find out their perception and other background characteristics responsible for drop out. A facility based descriptive type of observational study, cross-sectional in design was carried out among 329 HCWs of a tertiary care hospital in Kolkata from 16th March- 12thApril, 2021using a pre-designed, pre-tested, structured questionnaire. The study population selected by simple random sampling technique. Data was analysed using Microsoft Excel 2010 and SPSS v25.0 in the form of descriptive statistics and binary logistic regression. About 44.1% of the study population didn’t take the COVID-19 vaccine. Socio-demographic factors like age, gender, religion, education, occupation,perception regarding necessity of vaccination, vaccine efficacy, dose and contraindication, safety in humans and role in future infections were significantly associated with drop out. There was a high proportion of vaccine drop out among health care workers. Different modifiable perceptions with socio-demographic factors had played important roles in COVID-19 vaccination drop out. As the global threat of COVID-19 continues, greater efforts through campaigns that target HCWs are needed to improve the intention of professionals’ vaccine acceptance.

Association of Alcohol Use Diagnostic Codes in Pregnancy and Offspring Conotruncal and Endocardial Cushion Heart Defects

Background The pathogenesis of congenital heart disease (CHD) remains largely unknown, with only a small percentage explained solely by genetic causes. Modifiable environmental risk factors, such as alcohol, are suggested to play an important role in CHD pathogenesis. We sought to evaluate the association between prenatal alcohol exposure and CHD to gain insight into which components of cardiac development may be most vulnerable to the teratogenic effects of alcohol. Methods and Results This was a retrospective analysis of hospital discharge records from the California Office of Statewide Health Planning and Development and linked birth certificate records restricted to singleton, live‐born infants from 2005 to 2017. Of the 5 820 961 births included, 16 953 had an alcohol‐related International Classification of Diseases , Ninth and Tenth Revisions (ICD‐9; ICD‐10 ) code during pregnancy. Log linear regression was used to calculate risk ratios (RR) for CHD among individuals with an alcohol‐related ICD ‐9 and ICD10 code during pregnancy versus those without. Three models were created: (1) unadjusted, (2) adjusted for maternal demographic factors, and (3) adjusted for maternal demographic factors and comorbidities. Maternal alcohol‐related code was associated with an increased risk for CHD in all models (RR, 1.33 to 1.84); conotruncal (RR, 1.62 to 2.11) and endocardial cushion (RR, 2.71 to 3.59) defects were individually associated with elevated risk in all models. Conclusions Alcohol‐related diagnostic codes in pregnancy were associated with an increased risk of an offspring with a CHD, with a particular risk for endocardial cushion and conotruncal defects. The mechanistic basis for this phenotypic enrichment requires further investigation.

Demographic Factors and Job Characteristics Associated With Burnout in Chinese Female Nurses During Controlled COVID-19 Period: A Cross-Sectional Study

Background: To investigate the prevalence of burnout syndrome among Chinese female nurses during the controlled coronavirus disease 2019 (COVID-19) period and explore its associated socio-demographic factors and job characteristics.Methods: With the multistage, stratified sampling method, a cross-sectional online survey was conducted from September to October 2020 in China. The survey tool included revised Maslach Burnout Inventory (MBI) with 15 items, socio-demographic and job characteristics. Univariate logistic regression analysis and multivariate factor logistic regression analysis were used to identify the risk factors for burnout of female nurses.Results: During controlled COVID-19 period in China, the overall prevalence of burnout symptoms among Chinese female nurses was 60.2% with a breakdown in severity as follows: 451 (39.8 %) mild, 163 (14.4%) moderate, and 68 (6.0%) severe burnout. Little variance was reported for burnout symptoms according to job tenure (Waldχ2 = 14.828, P < 0.05,odds ratio [OR] <1), monthly salary income (Waldχ2 = 12.460, P < 0.05, OR <1), and night shift (Waldχ2 = 3.821, P < 0.05, OR > 1).Conclusion: Burnout symptoms among Chinese female nurses were prevalent and associated with job tenure, monthly salary income, and night shift. Female nurses who were with shorter job tenure, worked at night shifts, and had lower monthly salaries tended to exhibit increasing high-level burnout than their counterparts. This study serves as an implication for administrators and policy-makers to improve the work conditions of nurses for promoting overall healthcare service quality.

The impact of demographic factors on numbers of COVID-19 cases and deaths in Europe and the regions of Ukraine

The accumulated numbers of COVID-19 cases and deaths per capita are important characteristics of the pandemic dynamics that may also indicate the effectiveness of quarantine, testing, vaccination, and treatment. The statistical analysis based on the number of cases per capita accumulated to the end of June 2021 showed no correlations with the volume of population, its density, and the urbanization level both in European countries and regions of Ukraine. The same result was obtained with the use of fresher datasets (as of December 23, 2021). The number of deaths per capita and per case may depend on the urbanization level. For European countries these relative characteristics decrease with the increase of the urbanization level. Opposite trend was revealed for the number of deaths per capita in Ukrainian regions.