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Distress and Wellbeing among General Practitioners in 33 Countries during COVID-19: Results from the Cross-Sectional PRICOV-19 Study to Inform Health System Interventions.

Claire CollinsEls ClaysEsther Van PoelJoanna CholewaKatica TripkovićKatarzyna NesslerSégolène de RouffignacMilena Šantrić-MilićevićZoran BukumiricLimor AdlerCécile PonsarLiubove MurauskieneZlata Ožvačić AdžićAdam WindakRadost AssenovaSara J Willems
Published in: International journal of environmental research and public health (2022)
Emerging literature is highlighting the huge toll of the COVID-19 pandemic on frontline health workers. However, prior to the crisis, the wellbeing of this group was already of concern. The aim of this paper is to describe the frequency of distress and wellbeing, measured by the expanded 9-item Mayo Clinic Wellbeing Index (eWBI), among general practitioners/family physicians during the COVID-19 pandemic and to identify levers to mitigate the risk of distress. Data were collected by means of an online self-reported questionnaire among GP practices. Statistical analysis was performed using SPSS software using Version 7 of the database, which consisted of the cleaned data of 33 countries available as of 3 November 2021. Data from 3711 respondents were included. eWBI scores ranged from -2 to 9, with a median of 3. Using a cutoff of ≥2, 64.5% of respondents were considered at risk of distress. GPs with less experience, in smaller practices, and with more vulnerable patient populations were at a higher risk of distress. Significant differences in wellbeing scores were noted between countries. Collaboration from other practices and perception of having adequate governmental support were significant protective factors for distress. It is necessary to address practice- and system-level organizational factors in order to enhance wellbeing and support primary care physicians.
Keyphrases
  • primary care
  • healthcare
  • cross sectional
  • public health
  • electronic health record
  • big data
  • general practice
  • psychometric properties
  • mental health
  • systematic review
  • machine learning
  • case report
  • quality improvement