Mental health among nursing home staff during the Covid-19 pandemic: results from a prospective study

Corna, Laurie and Annoni, Anna Maria and Fiordelli, Maddalena and Ceschi, Alessandro and Bernasconi, Enos and Sallusto, Federica and Albanese, Emiliano (2021) Mental health among nursing home staff during the Covid-19 pandemic: results from a prospective study. In: 10a Giornata della Ricerca in Medicina Umana della Svizzera italiana, 25.06.2021, Lugano. (Unpublished)

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Introduction The Covid-19 pandemic has presented serious challenges to nursing homes, both in terms of resident morbidity and mortality, but also uncertainties and stressful working conditions that can take a toll on the mental health of staff. We estimate the prevalence of depressive symptomatology and occupational burnout among nursing home staff after the second wave. Methods COV-RISK is an interdisciplinary, multi-site prospective cohort study of nursing home staff and residents in Canton Ticino. All staff members of the nine institutes involved were invited to participate in both serology testing and a longitudinal survey to assess the implications of the pandemic for their mental health. Mental health is assessed using Depression Anxiety Stress scales-21 (DASS-21) and the Abbreviated Maslach Burnout Inventory (aMBI). Results 546 staff members are currently enrolled in the study. Among those who completed the first (n=369) and second (n=253) monthly follow up, depressive symptoms are widespread with moderate, severe and extremely severe symptoms reported by 24%, 6% and 2% of the sample, respectively. Nearly one-quarter of the sample (24%) reports moderate to severe emotional exhaustion, an aMBI subscale. Using follow up data currently being collected, we will look at changes over time in these outcomes, as well as the role of social support, social provision and risk perceptions. Conclusions A considerable proportion of staff members report at least moderate depressive symptoms and emotional exhaustion. Understanding the evolution of these outcomes, and their predictors, is essential for support these essential workers.

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