How Transformational Leadership Affects the Off-work Recovery of Daily Personal Energy Resources via Work Engagement: Resource and Demand-based Pathways.

This study focuses on the transformational leadership-work engagement relationship by investigating resource and demand pathways for daily off-work recovery and employee wellbeing (EWB). While previous research highlighted how transformational leadership energizes employees to engage at work, energy is a finite resource requiring daily restoration for EWB. Yet, how the leader's energizing effect relates to daily employees' recovery remains unknown. Following job demands-resource-recovery theory, we test two pathways that relate the transformational leadership-work engagement relationship to daily employee recovery: (a) Resource-based via resource-building, (b) demand-based via increased demands. Utilizing a 10-day, two daily measurement (N = 88) study, multilevel path analyses revealed: transformational leadership predicted via work engagement (b = .17, p < .05) role clarity (b = .56, p < .01), then positive (b = .39, p < .01), and negative work-nonwork spillover (b = -.38, p < .01). Positive work-nonwork spillover predicted recovery positively (b = .25, p < .01), negative work-nonwork spillover negatively (b = -.40, p < .01). Recovery predicted EWB for positive (b = .38, p < .01) and for negative (b = -.43, p < .01) affect. Work engagement predicted workload (b = .35, p < .01), further negative (b = .33, p < .01) and positive work-nonwork spillover (b = -.16, p < .01), hampering EWB. As one pathway effect might cancel the other, the main effect of transformational leadership on EWB was not significant in the integrative model (p > .05). Results highlight dark and bright sides of the transformational leadership-work engagement relationship regarding daily recovery.

Scales in pain control in patients post-ictus with aphasia: A comparative study.

The assessment of pain and its control is a key point in the care received by patients during their hospital stay, with the nurse being an essential figure, especially in stroke patients with secondary aphasia, which makes it difficult to verbalise pain. The aim of this study was to find out whether the use of a specific pain control scale and the more precise administration of analgesic treatment has a positive impact on indicators such as average stay length, dependence degree and agitation episodes. By means of a longitudinal randomised group design study, within a two-week period, we compared the management and recording of pain using a Pain Indicator Behaviour Scale (N = 41) and a Verbal Numerical Scale or Visual Analogue Scale (N = 39), in patients with language impairment after suffering a stroke who were admitted to the Stroke Unit of the Neurology Department of the Hospital Clínic de Barcelona. The results obtained, despite being in line with what was conjectured, are not statistically significant. However, some statistical models have shown the usefulness of some factors (level of training, nurses experience, previous episodes of stroke, among others) in the prediction of a better evolution, with a predictive capacity between 20-37%, which justifies the necessity of further studies and reinforces the role of the variables studied in the care of this type of patient.

Socio-economic and psychological impact of COVID-19 pandemic in a Spanish cohort BIOVAL-D-COVID-19 study protocol.

OBJECTIVE: SARS-CoV-2 outbreak has a negative psychological impact among general population. Data comparing mental health status before and during the outbreak is needed. The BIOVAL-D-COVID-19 study assess the socio-economic and psychological impact of the COVID-19 pandemic and lockdown in a representative sample of non-institutionalized Spanish adult population, and estimate the incidence of mental health disorders, including suicidal behaviours, and possible related factors. METHOD: Observational longitudinal study including two online surveys: baseline survey (T0) performed during 2019 and follow-up survey (T1) conducted 12-month later. The latter included nine sections: socio-demographic, health status, mental health, employment conditions and status, material deprivation, use of healthcare services, intimate partner violence and resilience. Four of the nine sections are administered in T0 and T1 assessments. Longitudinal data analyses will estimate adjusted incidence rates of mental health disorders using Poisson regression models. Risk and protective factors will be analysed through multiple logistic regression models.