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The Upshot of the SARS-CoV-2 Pandemic on Nursing Assistants: Evaluating Mental Health Indicators in Huelva.

Francisco-Javier Gago-ValienteEmilia Moreno-SánchezEmilia Vélez-MorenoMaria-de-Los-Angeles Merino-GodoyJesús Sáez-PadillaFrancisco de Paula Rodríguez-MirandaEmília Isabel Martins Teixeira da CostaLuis-Carlos Saenz-de-la-TorreAdrián Segura-CamachoMaría-Isabel Mendoza-Sierra
Published in: Journal of clinical medicine (2022)
Healthcare professionals who work in front-line situations are among those under the highest risk of presenting negative mental health indicators. We sought to assess the prevalence of low personal realization, emotional exhaustion, and depersonalization as well as probable non-psychotic psychiatric pathologies during the pandemic in nursing assistants in the city of Huelva (Spain), and to study the association between these mental health indicators and sociodemographic and professional variables. A cross-sectional descriptive investigation with a quantitative approach was used. A representative sample of these professionals, consisting of 29 men and 284 women, completed the GHQ-12 questionnaire, including sociodemographic data and the MBI-HSS questionnaire, collecting information on situations of contact with SARS-CoV-2. Data analysis was conducted, and correlations were established. We found that emotional exhaustion, depersonalization and probable non-psychotic, psychiatric pathologies were related to contact with SARS-CoV-2. Moreover, personal realization, depersonalization and emotional exhaustion were related to just gender. We conclude that nursing assistants from public hospitals in the city of Huelva who had contact with patients with SARS-CoV-2 in the workplace, showed poor mental health indicators than those who did not come into contact with infected individuals.
Keyphrases
  • mental health
  • sars cov
  • data analysis
  • respiratory syndrome coronavirus
  • mental illness
  • cross sectional
  • bipolar disorder
  • healthcare
  • risk factors
  • coronavirus disease
  • psychometric properties
  • big data
  • machine learning