RESUMEN
BACKGROUND: Nurses and social workers are two common professions with a university degree working within municipal nursing care and social welfare. Both groups have high turnover intention rates, and there is a need to better understand their quality of working life and turnover intentions in general and more specifically during the Covid-19 pandemic. This study investigated associations between working life, coping strategies and turnover intentions of staff with a university degree working within municipal care and social welfare during the Covid-19 pandemic. METHODS: A cross-sectional design; 207 staff completed questionnaires and data were analyzed using multiple linear regression analyses. RESULTS: Turnover intentions were common. For registered nurses 23% thought of leaving the workplace and 14% the profession 'rather often' and 'very often/always'. The corresponding figures for social workers were 22% (workplace) and 22% (profession). Working life variables explained 34-36% of the variance in turnover intentions. Significant variables in the multiple linear regression models were work-related stress, home-work interface and job-career satisfaction (both for the outcome turnover intentions profession and workplace) and Covid-19 exposure/patients (turnover intentions profession). For the chosen coping strategies, 'exercise', 'recreation and relaxation' and 'improving skills', the results (associations with turnover) were non-significant. However, comparing the groups social workers reported that they used 'recreation and relaxation' more often than were reported by registered nurses. CONCLUSIONS: More work-related stress, worse home-work interface and less job-career satisfaction together with Covid-19 exposure/patients (Covid-19 only for turnover profession) increase turnover intentions. Recommendations are that managers should strive for better home-work interface and job-career satisfaction, monitor and counteract work-related stress to prevent turnover intentions.
RESUMEN
Neurons regulate the activity of blood vessels through the neurovascular coupling (NVC). A detailed understanding of the NVC is critical for understanding data from functional imaging techniques of the brain. Many aspects of the NVC have been studied both experimentally and using mathematical models; various combinations of blood volume and flow, local field potential (LFP), hemoglobin level, blood oxygenation level-dependent response (BOLD), and optogenetics have been measured and modeled in rodents, primates, or humans. However, these data have not been brought together into a unified quantitative model. We now present a mathematical model that describes all such data types and that preserves mechanistic behaviors between experiments. For instance, from modeling of optogenetics and microscopy data in mice, we learn cell-specific contributions; the first rapid dilation in the vascular response is caused by NO-interneurons, the main part of the dilation during longer stimuli is caused by pyramidal neurons, and the post-peak undershoot is caused by NPY-interneurons. These insights are translated and preserved in all subsequent analyses, together with other insights regarding hemoglobin dynamics and the LFP/BOLD-interplay, obtained from other experiments on rodents and primates. The model can predict independent validation-data not used for training. By bringing together data with complementary information from different species, we both understand each dataset better, and have a basis for a new type of integrative analysis of human data.