Psychological Contracts: A Feature Based Approach to Understanding Transactional and Relational Contracts

Feature-based measures have been suggested as a solution to some of the many issues associated with measuring psychological contracts. This study involved the refinement of an existing feature-based measure, including adding and rewriting items, to provide a concise but comprehensive and generalizable measure of transactional and relational psychological contracts. Data were collected from an online sample of full-time employees. Analyses revealed three main psychological contract feature dimensions (transactional, individual relational, and group relational) clustered into three contract types (predominantly transactional, predominantly relational, and balanced). Moreover, the balanced contract type had the highest mean score on positive workplace outcome variables, such as commitment, engagement, and organizational citizenship behaviours. The predominantly transactional contract type had the lowest mean score for each of these variables, as well as the highest mean score for turnover intentions. Theoretical and practical implications for this measure, as well as future directions for research, are discussed

A longitudinal person-centred investigation of commitment in newcomers to the military

Organizational commitment is a force that binds individuals to their company through their desire, obligation, and need to stay. Employees who are committed to the organization are more likely to demonstrate higher engagement, greater satisfaction, and fewer intentions to leave their company. Research has also demonstrated that investigating how each of the three forms of commitment – affective, normative, and continuance – interact allows for better prediction of employee outcomes. Using person-centred approaches, previous research has shown that there are typically five to seven profiles of commitment, and that membership in these profiles has implications for employee behaviours. However, little research has examined how these profiles emerge and develop over time in samples of newcomers. The current research used archival data collected by the Canadian Armed Forces to investigate the development of commitment over the first year of employment with the military. Two samples were analyzed – one cross-sectional sample of employees at the end of their Basic Training experience (N = 3998) and one longitudinal sample of participants undergoing Occupational Training (N = 636). A person-centred approach to data analysis was adopted. Latent profile analyses demonstrated a four-profile solution in the Basic Training sample and a six-profile solution in the Occupational Training sample. Further, a latent transition analysis in the longitudinal data showed that membership in commitment profiles was relatively stable over the six-month time lag. These profiles were examined in relation to a number of antecedents and outcomes, with results indicating that value fit and social support were significant predictors of profile membership, and that turnover intentions and levels of well-being differed across profiles. These results have implications for person-centred commitment research. First, the differences in the profiles extracted in the Basic Training and Occupational Training samples suggest that time may be an important factor in the development of commitment. Further, results for the longitudinal sample suggested that, once profiles form, they become stable. This research validated previous findings on commitment profiles in military samples. Practical implications, limitations, and future directions are discussed

Evidence for a bifactor structure of the Scales of Psychological Well-being using exploratory structural equation modeling

This research investigates the much-debated factor structure of the 54-item version of Ryff’s (1989) Scales of Psychological Well-being (SPWB). Using two samples (n1 = 573; n2 = 449) of undergraduate university students, we apply confirmatory factor analysis (CFA) along with recently developed exploratory structural equation modeling (ESEM) techniques to evaluate several unidimensional and multidimensional models identified in previous research, as well as a new bifactor model. In a bifactor model, items load directly on both a global and a specific factor; when tested using ESEM, cross-loadings on other specific factors are also permitted and are targeted to be as close to zero as possible. After comparing various ESEM and traditional CFA models, the results indicate that a bifactor model estimated using ESEM provided the best fit to the data. Most items were found to reflect the global factor, but some items failed to reflect the intended specific factor. Thus, the 54-item version of the SPWB appears to be a good measure of overall psychological well-being, but may need refinement as a measure of the intended specific factors, at least among young adults. The benefits of applying ESEM to investigate the factor structure of the SPWB in other populations are discussed

Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation: As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and developm nt investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding: Bill & Melinda Gates Foundation. © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licens

Multi-omic longitudinal study reveals immune correlates of clinical course among hospitalized COVID-19 patients

The IMPACC cohort, composed of >1,000 hospitalized COVID-19 participants, contains five illness trajectory groups (TGs) during acute infection (first 28 days), ranging from milder (TG1-3) to more severe disease course (TG4) and death (TG5). Here, we report deep immunophenotyping, profiling of >15,000 longitudinal blood and nasal samples from 540 participants of the IMPACC cohort, using 14 distinct assays. These unbiased analyses identify cellular and molecular signatures present within 72 h of hospital admission that distinguish moderate from severe and fatal COVID-19 disease. Importantly, cellular and molecular states also distinguish participants with more severe disease that recover or stabilize within 28 days from those that progress to fatal outcomes (TG4 vs. TG5). Furthermore, our longitudinal design reveals that these biologic states display distinct temporal patterns associated with clinical outcomes. Characterizing host immune responses in relation to heterogeneity in disease course may inform clinical prognosis and opportunities for intervention